After receiving candidate SMILES strings from the LLM, they are validated by RDKit, an open-source cheminformatics toolkit. We first ensure the strings represent valid molecules through RDKit's Chem.MolFromSmiles function followed by Chem.SanitizeMol. This method checks the molecular structure's validity and adherence to standard conventions, including incorrect valency. Thereafter, the valid molecules are converted to canonical form, as SMILES are not bijective mappings. The canonical SMILES undergo further filtering, elim- inating duplicates and removing instances where the unmodified parent appears within the generated set. This ensures that unique and valid SMILES appear in the list of generated molecules (and only these are considered in the evaluation metrics). We evaluated the resulting molecules primarily using three metrics, similar to previous works: Tanimoto similarity, validity ratio, and chemical diversity.