To investigate the baseline behaviour of our approach, we re-implemented the first experiment conducted by Langevin et al. and compared the results to their method SAMOA. Whereby 17 drug discovery relevant scaffolds were extracted from SureChEMBL with between 1 to 5 specified attachment points per scaffold used for scaffold-constrained molecule generation. We pre-trained an RNN with the same hyperparameters and training dataset as provided by the authors. Note that the training dataset does not contain any molecules with any of the 17 validation set scaffolds. Once trained, 10,000 scaffold-decorated molecules were sampled and the validity and uniqueness were measured as shown in Figure with examples shown in B3. For 16/17 scaffolds, PromptSMILES generates a higher fraction of valid de novo SMILES, however, for only 1 in 17 scaffolds does it generate a higher fraction of unique de novo SMILES. This is expected due to the limitation introduced by the iterative nature of prompt completion earlier described in the Methods: after decoration of the first attachment point the molecule is likely considered already complete for future iterations. This could potentially improved by increasing sampling temperature, however, we proposed to see if the model was able to overcome this limitation by transfer learning to this new task via RL by using a simple representative objective task. The model was trained using the REINVENT algorithm to maximize the reward returned, whereby 1 was provided as the reward if a molecule was valid and unique, and 0 if the molecule was invalid or non-unique. The first 10,000 de novo SMILES generated are additionally plotted in Figure with examples shown in B4. PromptSMILES+RL is able to increase the unique fraction of SMILES generated in all cases and beyond the baseline SAMOA method for 15/17 scaffolds while maintaining a high fraction of valid SMILES. Note that performance of different PromptSMILES parameters can be seen in B1 and B2. This indicates that purely prompt-based conditional generation can be used to decorate scaffolds of multiple attachment points (up to 5 in this experiment). From here on, PromptSMILES refers to PromptSMILES with RL for fine-tuning and conditional optimisation of a specified objective.