A general algorithm for the prioritization and selection of plates for high-throughput screening is presented. The method uses a simulated annealing algorithm to search through the space of plate combinations for the one that maximizes some user-defined objective function. The algorithm is robust and convergent, and permits the simultaneous optimization of multiple design objectives, including molecular diversity, similarity to known actives, predicted activity or binding affinity, and many others. It is shown that the arrangement of compounds among the plates may have important consequences on the ability to design a well-targeted and cost-effective experiment. To that end, two simple and effective schemes for the construction of homogeneous and heterogeneous plates are outlined, using a novel similarity sorting algorithm based on one-dimensional nonlinear mapping.