This paper proposes a strategy for content-based image retrieval, which combines unsupervised feature learning (UFL) with the classical bag-of-features (BOF) representation. In BOF, patches are usually represented using standard classical descriptors (i.e., SIFT, SURF, DCT, among others).We propose to use UFL to learn the patch representation itself. This is achieved by applying a topographic UFL method, which automatically learns visual invariance properties of color, scale and rotation from an image collection. The learned image representation is used as input for a multimodal latent semantic indexing system, which enriches the visual representation with semantics from image annotations. The overall strategy is evaluated in a particular histopathology image collection retrieval task, showing that the learned representation has a positive impact in retrieval performance for this particular task.