Memory search is a basic level cognitive task that plays an instrumental role in producing many human behaviors. As such, we have investigated the mental states and mechanisms of human subjects’ analogical memory search in order to effectively model them in a computational problem solver. Three sets of these mental states and mechanisms seem to be important, regardless of the task domain. First, subjects use knowledge goals as a filter when looking for relevant experiences in memory. Second, as an additional retrieval filter, they use a similarity metric that finds a solution in memory whose most important weakest preconditions are satisfied in the current state. This metric requires the explicit representation of the reasoner’s belief of the relative importance of the preconditions, and introspection and adjustment of those beliefs through the comparison of actual and expected performance can be used to improve the memory search. Third, by explicitly representing how much search the reasoner has undertaken and its required threshold for the exactness of the match for the retrieved memory, it can dynamically adjust its memory search based on the contents of its knowledge-base.