Figure shows the score achieved when running baseline optimization (de novo generation) or constrained to linking two, three or even four sub-structures of the reference compounds as fragments. Firstly, constraining generation to a larger number of predefined fragments increases basal similarity to Atorvastatin at Epoch 0 as expected, depicting the benefit of specifying apriori knowledge of chemistry if known or desired. Secondly, regardless of the number of fragments, RL is still able to learn how to improve the reward and increase similarity to Atorvastatin. Interestingly, Figure and shows that the use of two fragments eventually achieves a higher score than the use of three fragments. This also corresponds to a switch in approach from Algorithm 2 to 3 which is conceptually more difficult due to a reliance on the stochasticity of the CLM with respect to the placement of fragments to the linker. Additionally, B14 shows that the CLM still learns to generate the unspecified fragments. But, specifying more apriori knowledge with four fragments still achieves a higher score overall and could prove a useful capability in practice.