Our evaluation across molecular weight ranges demonstrates the model's adaptability and the improvement cycle's effectiveness in enhancing performance on diverse structures. The cycle significantly improves identification accuracy, with notable results even when using just 10 analogs for weight ranges covered in initial training. For the ZINC dataset (250-350 Da), the IC increased perfect Tanimoto matches from 50% to 73% with 10 analogs, and up to 78% with 30 analogs. In the PubChem dataset (0-250 Da), the IC improved perfect matches from 14% to 45% with 10 analogs, surpassing the 44% achieved by the model fine-tuned on the entire PubChem dataset. Multinomial sampling consistently outperforms greedy sampling in identifying correct molecules, achieving up to 96% and 68% accuracy within the top 3 candidates for the ZINC and PubChem dataset, respectively. However, selecting the single most accurate candidate remains a challenge. The following section explores an additional step to address this.