Firstly, a study can run fewer transformations overall for high precision results when using clustering (Fig. ) in the limit where reference ligands exist for each of the clusters chosen. Secondly, the rules that compose the overall similarity score in LOMAP also have veto power, meaning that a rule can result in a similarity score of zero. Without clustering or filtering before optimization, edges without similarity to the entire set cause optimization failure. For example, consider a group of ligands that can be divided into sets of alchemically similar ligands. We called these groupings similarity substructures, which are visible in the heatmaps output in HiMap (Figs. and). Without clustering, a design may only weakly connect the similarity substructures via one or a few errorprone edges. The few edges connecting similarity substructures could require complex transformations such as a charge change, ring breaking, or a different binding mode. With clustering, the overall complexity of planned transformations is reduced. Alternatively, HiMap can optimize the design including all similarity substructures without clustering as long as sufficiently similar transformations exist so that Eq. 9 is satisfied.