Under strict data splitting, where the model is required to predict entirely new and unique chemical structures -thereby demonstrating its true generalization capability -baseline models exhibited significantly higher errors compared to random splitting. Additionally, model performance showed greater variability, as reflected in an increase in standard deviation. This can be attributed to the varying complexity of the test set, which depends on the degree of similarity between test compounds and those in the training set.