We then solve for the total number of edges, 𝑘, over the number of clusters, 𝑑, such that A value less than 1 indicates efficiency gains in the optimal design relative to the reference design. To compare to radial designs, we optimized designs with 𝑛 -1 edges and set the reference ligand to the ligand with the highest sum similarity to the rest of the set. For the optimal designs that we compared to LOMAP designs, we set the number of edges for optimized designs to the number of edges output in the LOMAP design. Error bars are the standard deviations in efficiency after running optimization three times each. Raw data is shown in Table . (B) Cost with clustering at 𝑛 ⋅ ln(𝑛) edges or constant expected MSE. The top plot shows the best-case savings or minimum cost where the clusters are equally balanced in size. The bottom plot shows the worst-case cost-benefit in the domain where clusters are least balanced. The minimum cluster size is defined as 𝑛 = 2. Edge costs are calculated for the high-performance cluster, UCI-HPC3, at $24∕edge (Methods and Materials).