To circumvent the limitations of baseline RL, we devise curricula and introduce 2 Curriculum Objectives to guide the agent to productivity: Tanimoto (2D) and ROCS (3D) shape-based similarity to the reference ligand. In the former, the rationale is that by teaching the agent to first generate compounds with 2D similarity to the reference ligand, subsequently generated compounds will have a greater likelihood of satisfying the docking constraint. The rationale for ROCS is identical except with 3D similarity to match the shape and electrostatics of the reference ligand. Triplicate baseline RL experiments with Tanimoto and ROCS components (using a scoring function comprised of Tanimoto/ROCS, docking, and QED together, respectively) were conducted for a fair comparison with CL. These baseline experiments did not improve agent productivity and similar training progress as the baseline shown in Fig. and Fig. is observed (Supplementary Fig. and). For the "Low" and "High" Tanimoto scenarios, the agent is immediately capable of generating compounds that satisfy the docking constraint (Fig. ). More specifically, although docking starts at a relatively low value (but higher than baseline RL) for the "Low" Tanimoto experiment, the agent quickly improves over the first 50 epochs and continues to do so for the remainder of the experiment. In the "High" Tanimoto scenario, docking starts at a score that exceeds the maximum score achieved by the baseline RL agent over 300 epochs and maintains productivity. The results are intuitive as enforcing the agent to first learn to generate compounds with higher 2D similarity to the reference ligand should increase the likelihood of satisfying the docking constraint. Similar observations are made when using ROCS as a Curriculum Objective (Fig. ). In both the "Low" and "High" scenarios, docking starts more favourably than baseline RL but unlike the Tanimoto experiments, the ROCS experiments start at a less favourable docking score. Firstly, these results are not completely surprising as training the agent to satisfy a 3D shape similarity objective will decrease the likelihood, relative to 2D similarity, in satisfying the docking constraint owing to more potential conformational discrepancies of the generated compounds compared to the reference ligand. Secondly, the agent still improves significantly over 100 and 50 epochs for the "Low" and "High" ROCS scenarios, respectively. These results convincingly demonstrate that the improvement in CL performance over baseline RL is attributed to the sequential nature of the CL objectives as opposed to the presence of the additional Curriculum Objectives only.