We developed a robust self-supervised clustering approach for classifying co-localized molecular images obtained using MSI. In this approach, data augmentation is combined with contrastive learning and selflabeling methods to train a deep CNN model without manual annotations. Systematic studies using a fully annotated mouse uterine tissue data and unannotated mouse brain tissue data demonstrate that the re-trained CNN model efficiently learns high-level molecular localization representations, which facilitate clustering of molecular images. Using a manually annotated benchmark dataset, we demonstrate that this approach achieves >90% classification accuracy. Meanwhile, clustering of a publicly available unannotated MSI data demonstrates the robustness of this approach and its applicability to different tissue types, image sizes, modes of ionization, instrument parameters, and data complexity.