Having established the value of using the ESP-Sim measure, the next step was to include it in the generative (DeepFMPO) method. An experiment was set-up to mimic a real-world scenario, where a set of lead compounds is optimized toward sweet spot criteria through a multi-parameter optimization process. Three different calculated properties (Molecular Weight, Polar Surface Area 22 and clogP ), commonly used in the optimization of leads to candidate drugs, were selected for this purpose. It should be noted that the choice of molecular properties was also selected for practical reasons facilitating reproducibility. Namely, there are methods to calculate them using RDKit. The aim of the set-up was to bias the generation of compounds to fulfill the criteria for the three calculated properties, while also maintaining their similarity in shape and electrostatics towards a known set of lead compounds. The agent in the reinforcement learning method was rewarded for producing valid molecules and got a higher reward when generating molecules with properties in the targeted ranges. Since this was a scaffold-hopping exercise, with the goal of identifying a new bioisosteric scaffold, the minimum and maximum target values for the three properties were centered around the corresponding values for compound 1 (i.e., 320<MW<420, 2.3<clogP<4. and).