Recent developments in Artificial Intelligence (AI) have led to new ways for users to search through vast information. However, users may have questions that are grounded in the real world that require spatial inference, for which language models are not well suited. Conversely, traditional spatial search methods, like spatial pattern matching, answer spatial reasoning questions correctly but are noise-intolerant, slow, and brittle. Presently, there are opportunities to integrate AI and spatial pattern matching to enable robust and flexible spatial search. This paper surveys existing spatial pattern matching methods, including the few that apply machine learning to the problem, discussing their efficiency and limitations, and describing opportunities to further enable spatial search through AI.