We initially tested this improvement cycle on a test set from the ZINC dataset to determine if further improvements could be achieved beyond the pretrained network. Figure presents the averaged Tanimoto similarity (a) and averaged correct sample probability (b) results for the ZINC test data before and after fine-tuning with different numbers of generated analogs. The multinomial sampling (MNS) approach, generating three candidates per target molecule, demonstrates remarkable effectiveness, identifying up to 96% of correct molecules within the top 3 candidates. For the MNS, we employed a molecular weight filter for the sampling process, accepting only molecules that fulfill these requirements. Greedy sampling also shows robust performance, correctly identifying up to 78% of molecules after fine-tuning. Model performance improves with up to 30 training analogs but plateaus or slightly declines with 50 or 100 analogs. This may result from the Mol2Mol model's tendency to generate up to 30 analogs per scaffold before switching, potentially impacting analog quality and finetuning effectiveness. For this experiment, we set the parameter for the number of samples per scaffold to 30. While the number of samples per scaffold is adjustable, we did not further investigate this parameter in the current study.