In the first sub-experiment, PromptSMILES is trained to optimize scaffold decoration to increase the predicted probability of D 2 activity (note exact reproduction was not possible due to the of lack diversity filter specification by the authors of LibIN-VENT, therefore, any difference in diversity filter used may also lead to slightly different results). Table shows the results of this experiment compared to those reported for LibINVENT, with the top 3 molecules shown in B10 and optimization curves shown in B11. We further split the results based the parameters used for PromptSMILES i.e., if the prompt undergoes highest CLM likelihood optimisation (optimise) or not, if the attachment point is randomly selected (shuffle) or not, and whether network updates are per prompt completion (multi) or not. For the task of optimizing D 2 predicted probability of activity without regard for synthesizability, PromptSMILES generates a higher number of successful compounds than LibINVENT in 5/8 configurations, resulting in a higher yield, and even with a higher average score in the resulting dataset in 2/8 configurations. Furthermore, if an example PromptSMILES configuration is run for more epochs to allocate a grace number of initial RL epochs to the transfer learning task of iterative prompting, Table shows that average score increases further.