• Utilizing the LSTM encoder described in , we extracted a lower-dimensional representation, denoted as z ∈ R 256 , for each TTD compound. Subsequently, we applied a Gaussian noise centered at 0 with a standard deviation of 2 (the choice of this standard deviation was a trade-off between mode collapsing and generation of invalid SMILES) to the z to generate 30,000 diverse representations. The LSTM decoder was then employed to generate the corresponding SMILES strings. Figure : Estimation of the near-neighborhood size N as a function of the heavy atom count for different similarity thresholds and a beam size of 30, 000. All the 821 TTD molecules were used as source compounds to estimate N . In each plot, the mean near-neighborhood size is depicted with a solid blue line, while its estimation N in dashed green line. The mean near-neighborhood size is calculated by averaging all the near-neighborhood sizes for a specific HAC. Note that the near-neighborhood size is defined as the number of compounds generated by beam search with a beam size of 30, 000 for which their similarity is greater or equal than the similarity threshold.