To improve perturbation map planning, we present the High Information Mapper, HiMap (Pitman and Mobley, 2022), a software package that generates optimal perturbation maps and clusters ligands into similar groups via unsupervised machine learning. We use the term optimal to mean the mathematical process of finding the solution that optimizes a statistical design criterion (further described in Background), rather than to mean that these graphs are best, colloquially. The optimization process can steer designs toward solutions that aid in the reduction of error. Indeed, optimizing the statistical architecture of RBFE perturbation maps dramatically improves the accuracy of free energy estimates .