The calculation for this dataset requires 8h on a i9-9820x CPU, using the 20 cores. DeepFMPO with the ESP-Sim measure generated a total of 6359 unique molecules, when terminated at 1000 epochs. About twothirds of those were sweet spot compounds. Hence, the agent generated compounds that have all three properties within the desired ranges. This number (ca 4000) is lower than when using a standard generative method facilitating the selection process, and a result of intentionally biasing using 3D-similarity. The evolution of the percentage of generated molecules that demonstrate properties within the target ranges during the training process is shown in Figure , displaying evidence of learning. A significant number of the generated compounds include the central scaffold of compound 2, and a number of those show near identical substitution pattern to compound 1. These bioisosteric compounds were observed in early epochs. One other nine-membered scaffold (3) was also represented among the generated, see Figure . When performing the same experiment but with simpler standard similarity measures (Morgan fingerprints, MACCS keys and Topological fingerprints), no compounds with the central scaffold of compound 2 (or 3) were generated. This provides an incentive for the use of DeepFMPO with ESP-Sim in scaffold-hopping exercises.