Self-index data structures for pattern matching based on Suffix Arrays and the Burrows-Wheeler Transform have recently grown popular. The fundamental operation in these self-indexes is the rank query: rank (i, c) requests the number of occurrences of symbol c before position i in a string. The wavelet tree is currently the data structure of choice for implementing rank queries. Current wavelet tree implementations encode the Burrows-Wheeler Transform as a hierarchy of binary strings, which they then store as RRR sequences; the RRR structure offers O (1) rank queries and zeroth-order entropy compression for binary strings. A generalisation of the RRR tecnique extends wavelet trees to have higher order encoding, that is, increased branching, in theory making traversal faster. To support the implementation of such a multiary wavelet tree, this thesis investigates the generalisation of RRR to sequences over small alphabets. We also analyse the use of concatenated bitmaps to represent sequences over small alphabets, thus allowing continued use of the binary RRR structure. Our results show that multiary wavelet trees are faster than their binary counterparts, but require large amounts of memory in the case of the generalised RRR. We also show binary RRR on concatenated bitmaps to be an effective, practical alternative.