Researchers from the University of Houston, the University of Texas McGovern Medical School, and the National Institute of Neurological Disorders and Stroke have developed mViSE, a visual search engine designed to analyze multiplex brain tissue images without requiring programming expertise. Published in *Scientific Reports*, this tool enables scientists to query slides visually, retrieving biologically similar cells or cellular neighborhoods within seconds. The motivation behind mViSE stems from the challenges of analyzing high-resolution spatial proteomics data, which captures over 30 protein markers simultaneously across millions of cells, generating massive datasets that conventional tools struggle to process efficiently. mViSE functions as an open-source plug-in for QuPath, a widely used digital pathology viewer. Users select a cell or tissue region of interest, prompting the system to retrieve visually and molecularly similar structures. The tool organizes protein markers into panels—each focusing on distinct tissue features—and employs unsupervised Vision Transformer (ViT)-based encoders to map tissue patches into a high-dimensional feature space. Training leverages contrastive learning and triplet loss to distinguish subtle cytoarchitectural differences, such as cortical layers. Notably, mViSE outperformed existing foundation models in delineating cortical layers, achieving an Intersection over Union (IoU) score of 0.70 and high true positive rates. A key finding was mViSE’s ability to accurately identify brain regions and cellular niches without explicit programming, demonstrating its potential for exploratory analysis. Single-cell retrieval accuracy was robust, with Top-1 scores reaching 0.90 across major cell types. However, computational demands are high, requiring GPU clusters for optimal performance. While currently validated on healthy rat brain tissue, future adaptations could extend mViSE to diseased or human tissue, pending retraining and ground truth validation. The team is also expanding its capabilities for comparative analyses, potentially aiding drug development and disease progression studies. The study underscores mViSE’s promise as an intuitive, programming-free solution for spatial proteomics, bridging a critical gap in high-throughput tissue analysis. Its open-source availability ensures accessibility for researchers, though clinical applications will require further refinement. Funding was provided by the National Institutes of Neurological Disorders and Stroke.