There are practical benefits to the HiMap clustering algorithm. By selecting 𝜖 at knees, as seen with the default 𝜖 of 0.95 in Fig. , we both avoid optimizing designs that include highly dissimilar nodes (which could break optimization, explained below) and preserve a low cluster number. For instance, a user could generate designs at an 𝜖 of 0.88 (Fig. ). However, the study could require added resources through ABFE simulations to connect designs or more experimental ligand data if a reference ligand with experimental data does not exist for a cluster.