to visualize the solution space diversity from the CL Tanimoto experiments. There is notable similarity, without overlap (as there is no scaffold overlap, see Supplementary Fig. ), between the compounds sampled from the "Low" and "High" scenarios (Fig. ). The results suggest that moderate optimization of similarity-based Curriculum Objectives (as in the "Low" scenarios) already significantly narrows the agent perceived solution space, in agreement with the cross-Tanimoto similarity distributions shown in Fig. . The similarity between the generated compounds from the "Low" and "High" experiments was quantified by calculating the cross-Tanimoto similarity between the two datasets (Supplementary Fig. ). The majority of cross-Tanimoto similarities is > 0.7, confirming that the generated compounds from both scenarios were sampled from areas 'close' in chemical space (Fig. ). Taken together, the observations in this section suggest that devising a curriculum and using Curriculum Objectives to guide the agent to a Production Objective facilitates knowledge retention that is exploited to achieve a state of productivity. However, there is an inverse relationship between using similarity-based Curriculum Objectives to enhance Production Objective optimization and intra-set diversity, imposing a trade-off when using CL over baseline RL.