Furthermore, we compared the effects of KD on the full model predictions using the original Chemformer and the Zinc model with original. Using KD, all models are significantly faster with respect to the randomly initialized models and Zinc fine-tuned models. However, this is paired with a decrease in top-1 and top-10 accuracy scores. Furthermore, significant increases in the percentage of invalid smiles generated are observed in the KD models. Specifically, the best performing Chemformer-based KD with optimal hyper-parameters for accuracy has a top-1 accuracy of 48.5% compared to 53.5% of the original Chemformer. A major explanation for this is the high percentage of invalid predictions, which range from 2.78 % up to 23.32% for the KD models. These models are on average half the size of the original implementation, but are as fast or faster than the randomly initialized model optimized for RCS, which is only 10% of the original model.