Best First Search in AI: How It Works, Types and Examples

Introduction

Best First Search in AI evaluates the available choices and moves toward the option that appears closest to the solution, rather than exploring every option. It does this using a heuristic, a simple estimate of how close the current state is to the goal.

By following these estimates, the search focuses on paths that are more likely to succeed, which is why it is called an informed search.

Here is what makes Best First Search in AI different from uninformed searches like BFS and DFS:

• Best First Search uses heuristics to guide the search, while BFS and DFS explore paths without guidance
• It expands nodes that appear closer to the goal, not just by order or depth
• BFS explores nodes level by level, and DFS goes deep along one path, even if it is not promising
• Best First Search can often find a solution faster by avoiding unnecessary paths

In this article, you’ll learn what Best First Search in AI is and how it uses heuristics to find the most promising paths. You’ll also explore step-by-step examples, understand key differences from other search techniques, and explore where it is applied in AI problems.

What is Informed Search in AI?

Informed search in AI is a strategy in which additional information, called heuristics, helps the system identify which paths are most likely to lead to a goal. The system does not treat all alternatives as the same; it rates each alternative based on these estimates and then focuses on the more promising paths. This method enables AI to navigate challenging search spaces better and arrive at solutions with reduced workload.

Using heuristics gives informed search a clear advantage over blind methods. By prioritizing the most promising paths, AI systems can achieve results faster and with greater accuracy. This strategy is particularly valuable in areas like planning, navigation, scheduling, and game AI, where selecting the right path early improves overall performance.

Here’s a surprising stat: A 2025 paper by Arxiv introducing iA* (a learning‑augmented A* / best‑first search method) reports an average 65.7% reduction in search area and 54.4% reduction in runtime compared with baselines, showing large efficiency gains while keeping near‑optimal paths.

Best First Search Explained

Now that you understand what an informed search is, let’s take a closer look at  Best First Search and see how it works in practice:

• Core Idea of Best First Search

The Best First Search algorithm always selects the most promising node from the current frontier and expands it first. It uses a heuristic function (often written as h(n)) to estimate how close a node is to the goal, and nodes with lower heuristic values are considered better candidates.

To manage these nodes, the algorithm typically uses a priority queue or open list that sorts nodes by their heuristic estimates, so the best candidate is processed next.

• Step-by-Step Algorithm

Best First Search follows a precise algorithm to find the most promising path. Here are the steps involved:

Step 1: The Best First Search algorithm begins by placing the initial node into a priority queue and repeatedly selecting the node with the lowest heuristic value.

Step 2: The selected node is then removed from the open list and checked against the goal condition.

Step 3: If the node does not match the goal, add its neighbors to the priority queue with their heuristic values.

Step 4: This cycle continues until the goal is reached or all nodes in the search space have been evaluated.

The flow follows the pattern: evaluate > select the best candidate > expand > check the goal, which provides a clear overview before any implementation.

• How Heuristics Guide the Search

Heuristics assign a score to each node indicating its proximity to the target. Best First Search uses this score to select the next node to explore.

Greedy Best First Search follows the same pattern as it relies on heuristics, but only considers the estimated distance to the goal while completely ruling out the distance from the starting point.

Examples of heuristics include straight‑line distance or Manhattan distance in pathfinding problems.

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A quick fact for you to remember: Best First Search offers O(b^m) time complexity (where b is the branching factor, m is max depth) and O(b^m) space complexity, better than uninformed searches like BFS (O(b^d)) but worse than binary search (O(log n)). Here’s the working flow of Best First Search in AI.

Greedy Best First Search vs Best First Search

Although Best First Search and Greedy Best First Search both rely on heuristics, the terms are often used interchangeably, which can be confusing. Let’s look at Greedy Best First Search: what it is, a simple example, and when it’s used.

Greedy Best First Search

Greedy Best First Search is an informed search that estimates the distance to the goal and selects the next node to explore accordingly. It differs from the general Best First Search in that it prioritizes nodes based solely on estimates, which can lead to faster search in many scenarios.

For example, in a fundamental pathfinding problem on a map, the algorithm evaluates nearby cities from the current location and routes to the city with the shortest straight-line distance to the target. This method is preferred when solution speed is more important than a perfectly optimal path.

It is common in applications like robot navigation, simple planning tasks, and video game pathfinding.

To highlight the differences between Greedy Best First Search and Best First Search, consider the following comparison:

Example of Best First Search Algorithm

So you have seen the difference between Best First Search and Greedy Best First Search. Let’s now look at an example to see how BFS works in practice:

Simple Tree Search Example

Consider a simple tree, and assume the goal is to reach node G from the starting node A. Each node is assigned a heuristic value, h(n), estimating the distance to the goal.

BFS evaluates nodes by selecting the one with the lowest h(n) at each step. For instance, if nodes B, C, and D are neighbors of A, and their heuristic values are B=3, C=2, D=4, the algorithm will expand C first since it has the lowest heuristic.

Trace the Search

Step by step, the search proceeds by expanding the most promising node, updating the open list with neighbors, and checking if the goal is reached:

1. Begin with node A at the top of the priority structure.
2. Determine the heuristic values for neighbors B, C, D, and insert them in the queue.
3. Choose node C (the one with the lowest h(n)) to be expanded next.
4. Proceed to add neighbors of C and update the priority queue based on their heuristic ratings.
5. Continue doing this until node G is selected and the goal condition is satisfied.

Code Snippet (Python)

Here’s a simple Python snippet illustrating Best First Search for a small graph:

import heapq
def best_first_search(graph, start, goal, heuristics):
open_list = []
heapq.heappush(open_list, (heuristics[start], start))
visited = set()
parent = {start: None}
while open_list:
_, current = heapq.heappop(open_list)
if current == goal:
path = []
while current:
path.append(current)\
current = parent[current]
return path[::-1]
visited.add(current)
for neighbor in graph[current]:
if neighbor not in visited:
heapq.heappush(open_list, (heuristics[neighbor], neighbor))
if neighbor not in parent:
parent[neighbor] = current
# Example graph
graph = {
  'A': ['B', 'C', 'D'],
  'B': ['E', 'F'],
  'C': ['G'],
  'D': ['H'],
  'E': [],
  'F': [],
  'G': [],
  'H': []
}
heuristics = {'A': 5, 'B': 3, 'C': 2, 'D': 4, 'E': 4, 'F': 6, 'G': 0, 'H': 7}
path = best_first_search(graph, 'A', 'G', heuristics)
print("Path found:", path)

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Heuristic Function in Best First Search

A good heuristic for the Best First Search algorithm can provide a fair estimate of the distance to the goal without overestimating the actual cost. In AI search, useful heuristics are often admissible, meaning they never guess a price higher than the actual minimum cost to reach the goal, and consistent, meaning their estimates behave logically across steps.

These properties help the search avoid unnecessary backtracking and keep the number of explored nodes low. Heuristics should also be simple enough to compute quickly so they do not outweigh the benefit they provide.

In practice, standard heuristic functions depend on the problem type. For spatial search tasks such as map navigation, straight‑line (Euclidean) distance is often used because it provides a lower bound on the actual distance.

In grid‑based environments where movement is limited to horizontal and vertical directions, Manhattan distance is a widely used estimate. For structured problems such as sliding tile puzzles, heuristics, such as the count of misplaced tiles or the sum of distances to their goal positions, help guide the search efficiently.

Time and Space Complexity

From a performance perspective, Best First Search can vary depending on the problem size and the growth of the search space. In the worst case, the algorithm may examine many nodes before reaching the goal, leading to a significant increase in time complexity for large graphs.

At the same time, Best First Search needs to store all generated but unexpanded nodes in a priority queue, which results in higher space complexity than depth-first approaches. This memory requirement is an essential consideration in problems with large search spaces or limited resources.

Highly Recommended Read: Understand how time and space complexity help you analyze an algorithm’s efficiency by measuring execution time and memory usage. This guide breaks down Big O notation with clear examples to help you write optimized, scalable code.

Advantages of Best First Search

By now, you have seen how Best First Search works, how heuristics influence its decisions, and what it costs in terms of time and memory. Now, let’s look at the key advantages that make this approach useful in real AI systems:

• Goal-Oriented Exploration

One of the main strengths of Best First Search is its strong focus on reaching the goal rather than exhaustively exploring the search space. The algorithm consistently favors nodes that appear more promising, helping it avoid being distracted by paths unlikely to lead to a solution.

This goal-oriented behavior is especially valuable in large or complex environments where exploring every possible option is not practical.

• Flexibility Across Problem Domains

Best First Search is not tied to a single type of problem structure. It can be applied to graphs, trees, maps, planning problems, and game environments with minimal changes to the core logic.

As long as a meaningful evaluation function is available, the same search framework can adapt to different domains, making it a flexible choice for applied AI.

• Early Discovery of Acceptable Solutions

In many real-world applications, finding a reasonably good solution quickly is more important than finding the perfect one. Best First Search often finds a valid solution early in the search process, even if it is not guaranteed to be optimal.

This property makes it suitable for time-sensitive systems such as navigation, real-time decision-making, and interactive applications.

Limitations of Best First Search

Best First Search offers several advantages, but several limitations should still be considered when deciding whether to use it.

• Dependence on Heuristic Quality

The effectiveness of Best First Search is tightly linked to the quality of the heuristic function. If the heuristic provides poor or misleading estimates, the algorithm may explore unproductive paths and miss better solutions.

In such cases, the search can behave inefficiently and may even perform worse than simpler uninformed methods, despite using additional information.

• No Guarantee of Optimal Solutions

Best First Search does not guarantee finding the optimal path unless additional constraints are imposed on the heuristic and evaluation strategy. The algorithm may select a suboptimal solution if it appears closer to the goal, since node selection is based on perceived promise rather than total path cost.

This limitation makes it unsuitable for problems where correctness and optimality are critical, such as cost-sensitive planning tasks.

• High Memory Consumption

Since Best First Search preserves a priority structure for all generated but unexplored nodes, its memory consumption can increase rapidly as the search space grows. In dense graphs or when branching factors are high, this storage requirement can at times become a bottleneck.

Compared to depth-first approaches, the algorithm is less memory-efficient, which limits its use in resource-constrained environments.

Applications of Best First Search in AI 

Let’s understand how Best First Search is used in practical AI systems:

1. Pathfinding and Navigation

Best First Search is typically used in pathfinding and navigation scenarios where an agent must reach a destination from a starting point in the most efficient way. In the case of robots and autonomous delivery, the search is directed toward the target using heuristics such as the straight-line or Manhattan distance.

This, in turn, enables the system to avoid unnecessary exploration of vast and irrelevant areas of the environment. It is also used in games and map-based navigation, where quick route discovery is essential.

2. Puzzle Solving

In puzzle-solving problems like sliding tile puzzles or maze-based challenges, Best First Search prioritizes states that appear closer to the solution. Heuristics such as misplaced tiles or estimated remaining moves guide the search effectively. 

This leads to fewer configurations being explored than with blind techniques. Accordingly, large state-space puzzles are more convenient to deal with.

3. Search and Optimization Tasks

Best First Search is used in search and optimization tasks where finding a good solution quickly matters more than guaranteed optimality. It is applied in scheduling, planning, and decision-making systems that rely on heuristic evaluations. 

By expanding the most promising candidates first, the algorithm efficiently narrows large search spaces. This makes it suitable for problems with time or resource constraints.

Variants and Related Informed Search Techniques 

Now, let’s look at a few informed search techniques that are closely related to Best First Search:

• A* Search

A* Search extends Best First Search by considering both the cost from the start node and the estimated cost to the goal. It evaluates nodes using a combined function, which helps balance speed and accuracy during the search.

When the heuristic does not overestimate, A* is guaranteed to find the lowest-cost path. Because of this property, it is used in pathfinding, robotics, and game development.

• Beam Search

Beam Search modifies Best First Search by limiting the number of nodes examined at each level. It does not retain all possible states; instead, it picks a specific number and discards the rest. This leads to lower memory use and faster searches in vast areas. Nevertheless, the trade-off is that early pruning may miss the optimal solution.

• Local Search Methods

Local search methods focus on improving a single solution rather than exploring a full search tree. Algorithms like hill climbing move step by step toward better neighboring states based on a heuristic. These methods are efficient for significant optimization problems, but can get stuck in local optima. Variants introduce randomness or memory to reduce this limitation.

Best First Search vs Other Search Algorithms

Before using Best First Search in your projects, it is also essential to compare it with local search algorithms to understand where it performs well and where it differs:

Relationship Between Best First Search and A* Search

Best First Search and A* Search are closely related because both rely on heuristics to guide the search toward a goal. Best First Search selects nodes based on how promising they appear with respect to the goal, using a heuristic estimate to decide which node to expand next. This search is strongly goal-directed, but it also means its behavior depends heavily on the accuracy of the heuristic.

A* Search builds on the same idea but adds more structure to the decision process. Instead of looking only at the estimated distance to the goal, it also considers the cost already spent to reach a node.

By combining these two values, A* reduces the chances of being misled by an overly optimistic heuristic. As a result, A* is generally more reliable than Best First Search, especially when finding the lowest-cost paths is essential.

Common Misunderstandings About Best First Search

There are a few common misunderstandings about Best First Search. Let’s clear them.

• Best First Search Always Finds the Best Path

One common belief is that Best First Search will always find the shortest path to the goal. In reality, it does not guarantee an optimal solution because it selects nodes solely based on heuristic estimates rather than actual path costs. If the heuristic is misleading or does not reflect actual cost, the algorithm may stop at a solution that looks good but is not the best.

• Heuristics Make the Algorithm Always Efficient

Another misunderstanding is that simply adding a heuristic automatically ensures efficient search. While heuristics can reduce the number of nodes examined, their quality is crucial.

A poor heuristic can misguide the search into unhelpful areas, leading to more work or suboptimal results than even uninformed methods.

• Best First Search Works Well Without Domain Knowledge

Best First Search works well across all problem types without customization. This is not true: designing a heuristic often requires domain knowledge to ensure it aligns with problem structure. Without that insight, the heuristic may fail to guide the search toward useful regions of the space.

• Heuristic Search Always Saves Memory

Because the algorithm focuses on promising paths, it is believed to use less memory than other search methods. In fact, Best First Search still maintains a priority (open list) of all generated but unexpanded nodes, and in large search spaces, this can consume substantial memory.

Key Takeaways

• Best First Search in AI uses heuristics to prioritize the most promising paths, making it faster and more goal-oriented than uninformed search methods like BFS and DFS
• It is highly flexible and applicable across pathfinding, puzzle-solving, and optimization tasks, enabling AI systems to find acceptable solutions quickly
• The efficiency of Best First Search in artificial intelligence is highly dependent on the quality of the heuristics; the better the heuristics, the less exploration is required, and the better the performance
• Despite being powerful, Best First Search is not always optimal and may require a lot of memory in the case of large search spaces; heuristic design and problem context need to be considered very carefully

Helpful Resources

• Alpha Beta Pruning in AI: Adversarial Search Algorithms
• What is Linear Search Algorithm?
• Binary Search Algorithms: Overview, When to Use, and Examples

FAQs

1. Which algorithm is best, DFS or BFS?

It depends on the problem. BFS guarantees the shortest path in unweighted graphs but uses more memory. DFS is memory-efficient but may go deep into unpromising paths.

2. What is the best-first search technique in AI?

The Best First Search is an informed search that applies heuristics to select the most promising node at each step, thereby moving towards the goal efficiently.

3. Is Best First Search optimal?

Not always. It depends on the heuristic. If the heuristic is not perfectly accurate, the algorithm may find a suboptimal solution.

4. How does heuristic affect performance?

A good heuristic reduces unnecessary node exploration and speeds up the search, while a poor heuristic can mislead the algorithm and increase computation.

5. What’s the difference between Best First and A?*

Best First only considers heuristic estimates to the destination. A* considers both the cost of the path from the start and the heuristic, making it more likely to find the lowest-cost path.

6. Can Best First Search be used in large state spaces?

Yes, but it may consume significant memory since all generated nodes are stored in a priority structure. Careful heuristic design is essential.

7. What are real-world examples?

It’s used in robot navigation, game pathfinding, puzzle solving, scheduling, and planning tasks where goal-directed search is valid.

8. When should I choose Best First over Greedy?

You should pick the Best First algorithm if you are looking for a less biased search that considers multiple criteria, not just proximity to the destination. It is more dependable in challenging environments.

9. How to implement Best First Search in Python?

Use a priority queue with heuristic values. Expand the node with the lowest heuristic first, adding neighbors with their heuristic scores. 

10. What are admissible heuristics?

Admissible heuristics never overestimate the actual cost to reach the goal, ensuring the search remains efficient and avoids misleading paths.

11. Does Best First backtrack?

Not explicitly. It might revisit already explored nodes if they become attractive again; however, the method of revisiting nodes used by Best First differs from that in DFS.

12. How is BFS different from informed search?

BFS explores all nodes level by level without guidance. Informed search, like Best First, uses heuristics to focus on paths more likely to reach the goal efficiently.