For all these plots, the chosen target size for each generation was 21 ± 5, which is roughly near the most populous region of the CheMBL database -on the lower end. The choice of training and test size ranges as reflected in the CheMBL molecules' size distribution is shown in Figure of the SI. We trained on smaller sizes to ensure quick training and efficient detection of generative modecollapse by monitoring the Tanimoto similarity score. A similar analysis is performed for generating molecules of a higher size (see figure ), to ensure good performance of the model even on increasing molecule size beyond the training range. The effect of regularization is more prominent at larger sizes, reducing the mean similarity to the nearest neighbor from 0.53 for the baseline to 0.51 after training. We also saw that the similarity distributions shift to the right with increasing size, which probably indicates that larger molecules tend to have more fragments in common with each other. However, the probability of finding multiple similar fragments on the same molecule decreases, which could explain the greater influence of regularization at higher target sizes.