To visualize the quality of the results, the binding poses of selected generated compounds are superimposed with the reference ligand (Fig. ). The binding poses retain the two hydrogen bond interactions with Ala 162, as enforced by the docking constraint. Furthermore, the superimposed binding poses demonstrate excellent agreement with the reference ligand, supporting plausibility. Thus, we show that using Tanimoto (2D) and ROCS (3D) shape-based similarities to the reference ligand as Curriculum Objectives can guide the agent to satisfy a complex Production Objective and the results demonstrate CL outperforms baseline RL given the same number of production epochs. Moreover, tuning the degree of Curriculum Objective optimization, as shown in the "Low" and "High" scenarios, provides direct control in guiding the agent to productivity.