Some authors have used "best-first search" to refer specifically to a search with a heuristic that attempts to predict how close the end of a path is to a solution, so that paths which are judged to be closer to a solution are extended first. This specific type of search is called **greedy best-first search**.^{[2]}

Efficient selection of the current best candidate for extension is typically implemented using a priority queue.

OPEN = [initial state]
while OPEN is not empty
do
1. Remove the best node from OPEN, call it n.
2. If n is the goal state, backtrace path to n (through recorded parents) and return path.
3. Create n's successors.
4. Evaluate each successor, add it to OPEN, and record its parent.
done

Note that this version of the algorithm is not *complete*, i.e. it does not always find a possible path between two nodes even if there is one. For example, it gets stuck in a loop if it arrives at a dead end, that is a node with the only successor being its parent. It would then go back to its parent, add the dead-end successor to the `OPEN`

list again, and so on.

The following version extends the algorithm to use an additional `CLOSED`

list, containing all nodes that have been evaluated and will not be looked at again. As this will avoid any node being evaluated twice, it is not subject to infinite loops.

OPEN = [initial state]
CLOSED = []
while OPEN is not empty
do
1. Remove the best node from OPEN, call it n, add it to CLOSED.
2. If n is the goal state, backtrace path to n (through recorded parents) and return path.
3. Create n's successors.
4. For each successor do:
a. If it is not in CLOSED: evaluate it, add it to OPEN, and record its parent.
b. Otherwise: change recorded parent if this new path is better than previous one.
done

Also note that the given pseudo code of both versions just terminates when no path is found. An actual implementation would of course require special handling of this case.