In a computer game, equipping a bot with a suitable algorithm to locate a human player is difficult. Besides the unpredictable moves made by the player, an unexplored map region poses additional constraints such as new obstacles and pathways that the bot needs to discover quickly. The design criteria of such moving target search (MTS) algorithms would typically need to consider computation efficiency and storage requirements. That is, the bot must appear to be “smart” and “quick” in order to enhance the playability and challenge posed by the game. These criteria, however, pose conflicting requirements. In this article, we study and evaluate the performance and behavior of two novel MTS algorithms, Fuzzy MTS and Abstraction MTS, against existing MTS algorithms in randomly generated mazes of increasing size. Simulations reveal that Fuzzy MTS and Abstraction MTS exhibit competitive performance even with large problem spaces.