Music loops are in increasing demand for music creation as well as live performance. There are numerous free and commercial options available for procuring and using loops from libraries and community repositories. A challenge commonly encountered by musicians is searching for loops that match their sonic requirements within the large space of available options. Musicians typically rely on metadata associated with loops, such as key and tempo, to narrow the search space and subsequently make selections using their own perceptual judgments. This process can be time-consuming and effort-intensive. In our work, we investigate whether AI audio models can assist musicians in the task to search loops with perceptual similarity. We develop LoopMatcher, a workflow that integrates two complementary AI models - one for efficient embedding-based retrieval (VGGish) and the other for perceptual similarity refinement (CDPAM) - to automatically narrow the search space and rank candidate loops given a reference loop. In a proof-of-concept validation study, we observe a Spearman correlation of 0.68 between algorithmic rankings and human perceptual judgments. Additionally, randomly selected loops consistently rank lowest, judged by both the system and participants. The results of our work provide encouraging outcomes that indicate strong correlations between our approach and human perceptual judgments, demonstrating that AI-assisted loop search shows promise and merits further investigation.