We propose an appearance-based loop closure detection algorithm based on binary features and a Bag-of-Words scheme. Unlike other approaches that build the visual dictionary offline, we introduce an indexing method for binary features, which, in combination with an inverted index, enable us to obtain loop closure candidates in an online manner. These structures are used in a discrete Bayes filter to select final loop candidates and to ensure temporal coherency between predictions. Our approach is validated using two publicly available datasets of outdoor environments and compared with the state-of-the-art FAB-MAP algorithm, showing very promising results and demonstrating that binary features can be used for visual loop closure detection.