We consider the familiarity problem: for given pat-tems21,...2, E {O,l}" and input y E (0, l}n one has to decide whether y is sufficiently close to one of the patterns with respect to Hamming distance.Errors are allowed and inputs of intermediate distance are excluded, when evaluating arbitrary memory models for the familiarity problem.We design neural circuits for various distributions and show that the memory complexity of our circuits (i.e., the logarithm of the total number of required circuits) is close to optimal, even when compared to arbitrary memory models.Moreover we obtain near-optimal results simultaneously for the resources memory complexity, depth and number of edges.Additionally the number of vertices can be minimized in a somewhat restricted setting.Circuits for the familiarity problem are considerably smaller in terms of memory complexity, number of edges or number of gates when compared to circuits for the conventional Hamming distance problem.*supported by DFG project Schn .503/l-2Permission to make digital or hard copies of all or p"rt of this work for personal or classroom use is