Many computational processes require efficient algorithms, those that both store and retrieve data efficiently and rapidly. In this paper, we evaluate a selection of data structures for storage efficiency, retrieval speed and partial matching capabilities using a large information retrieval dataset. We evaluate standard data structures, for example, inverted file lists and hash tables but also a novel binary neural network that incorporates superimposed coding, associative matching and row-based retrieval. We identify the strengths and weaknesses of the approaches. The novel neural network approach is superior with respect to training speed and partial match retrieval time.