In some cases, there may not be any specified or desired scaffold attachment points. Our approach can be trivially extended to this scenario by simply viewing every atom with available valence as being an attachment point. To demonstrated this, we reimplement the scaffold-constrained GuacaMol benchmarks proposed by . This benchmark specifies four scaffolds for scaffold-constrained generation and respective scoring functions to evaluate de novo molecules. The prior used was trained on the GuacaMol dataset and the optimization algorithm used is the same as in REINVENT, run for 500 epochs. For each scaffold, we specify every atom as an available attachment point (see Figure ). The results of this benchmark are shown in Table in comparison to other scaffold-constrained generative methods as reported. Here, as there are more attachment points and no diversity filters in the scoring functions, we run PromptSMILES with shuffle and prompt optimisation and only investigate the use of multiple RL updates or not. Although PromptSMILES gets the lowest overall score on the benchmark, it still scores highly with a comparable score of 0.88 or 0.91 and a comparable quality metric of 0.58 for the top 100 proposed compounds. However, there are some nuances to the use of SMILES prompts for these benchmark tasks. For example, for the factor Xa task, the similarity objective should require the model to connect two atoms of the substructure to form an oxazolidinone. However, this requires that the CLM appends a ring connection to one attachment point without closing it, followed by a closing ring connection to another attachment point at a later iteration. This process would result in an invalid SMILES string in-between iterations. This is a limitation of our current implementation which requires RDKit to parse a molecule at each iteration, rather than the concept of this approach. For, factor Xa, this can be circumvented by shortening the scaffold-prompt so that the CLM extends a single attachment point and can open and close a ring within one iteration (see Figure ). Table shows that by simplifying the factor Xa scaffold, the quality metric reaches state-of-the-art compared to other approaches albeit a limited comparison. The same nuance applies to macrocyclisation of lorlati, however, this can not be circumvented as easily and requires an implementation without requiring valid molecules at each iteration. This task is predominantly responsible for decreasing the average score (see B6 and B7). Interestingly, the use of multiple RL updates and therefore stronger optimisation ability increased the score but decreased the chemical quality of the top 100 compounds. This is suggestive of mode collapse which may require a diversity filter to circumvent. We also note that most objectives are optimized by approximately 300 epochs (see Figure ) which required 226 ± 163 minutes (246 ± 185 for multi) on a single NVIDIA RTX 4090 with the scoring functions utilising 1 CPU core. This is significantly more efficient than the reported value of MoLeR of 6 to 130 GPU hours . 0.41 (0.47) 1