To evaluate the impact of data set size on model performance, we downsampled the training and validation sets to create training sets containing 200, 500, 1,000, 2,000, 5,000, 10,000, and 20,000 unique molecules. For the downsampled datasets, we used a single data split for both random and K-Means splitting and trained five-ensemble models without any hyperparameter re-optimization.