For many diseases, molecular testing may provide rich information for both diagnosis and treatment planning. Combining or aggregating tissue morphology information with molecular data may improve the accuracy of patient matching. Nonetheless, molecular analyses, such as RNA sequencing, are expensive and time-consuming and may require additional tissue samples. Predicting gene expression values from whole slide images (WSIs) is limited by weakly supervised challenges due to the lack of pixel-level annotations for each tile in the WSI. On the other hand, searching for similar anatomic clues has been demonstrated to be a powerful unsupervised technology.