Associative memories are data structures addressed using part of the content rather than an index. They offer good fault reliability and biological plausibility. Among different families of associative memories, sparse ones are known to offer the best efficiency (ratio of the amount of bits stored to that of bits used by the network itself). Their retrieval process performance has been shown to benefit from the use of iterations. We introduce several families of algorithms to enhance the performance of the retrieval process inrecently proposed sparse associative memories based on binary neural networks. We show that these algorithms provide better performance than existing techniques and discuss their biological plausibility. We also analyze the required number of iterations and derive corresponding curves.