The current gold standard for interpreting patient tissue samples is the visual inspection of whole–slide histopathology images (WSIs) by pathologists. They generate a pathology report describing the main findings relevant for diagnosis and treatment planning. Search-ing for similar cases through repositories for differential diagnosis is often not done due to a lack of efficient strategies for medical case–based re-trieval. A patch–based multimodal retrieval strategy that retrieves sim-ilar pathology cases from a large data set fusing both visual and text information is explained in this paper. By fine–tuning a deep convolu-tional neural network an automatic representation is obtained for the vi-sual content of weakly annotated WSIs (using only a global cancer score and no manual annotations). The pathology text report is embedded into a category vector of the pathology terms also in a non–supervised approach. A publicly available data set of 267 prostate adenocarcinoma cases with their WSIs and corresponding pathology reports was used to train and evaluate each modality of the retrieval method. A MAP (Mean Average Precision) of 0.54 was obtained with the multimodal method in a previously unseen test set. The proposed retrieval system can help in differential diagnosis of tissue samples and during the training of pathol-ogists, exploiting the large amount of pathology data already existing digital hospital repositories.