Overall, we found that the E-CLM performed better than the standard CLM for the task of identifying the most active molecules, while minimizing the number of inactive molecules misclassified as "highly active". For the chosen threshold (0.4), the E-CLM had a false positive rate of 10.0% compared to 46.7% for the CLM for the same true positive rate (71.3%) (Supplementary Figs. and). Fine-tuning of all neural network weights performed better than keeping the weights of one of the two layers constant (Supplementary Figs. and). These results highlight the importance of choosing an appropriate pretraining method depending on the downstream task, e.g., data generation or classification.