Each data set D = {(x i , y i )} n i=1 is initially divided into two parts: an out-of-distribution (OOD) test set and an in-distribution (ID) data set, using various extrapolative splitting approaches (detailed in Section Splitters). To minimize label distribution shifts between ID and OOD sets while maintaining feature distribution differences (covariate shift), we implement an iterative splitting process. This process generates up to 100 candidate splits and selects those where the difference in the percentage of active compounds between ID and OOD sets falls below a predetermined threshold. We aim to obtain 10 qualifying splits, incrementally relaxing the threshold if necessary. For deterministic splitting methods, where controlling label distribution is not possible, we accept the resulting label distribution shift (Figure ). (2) Create distance matrix M ∈ R N ×512 using 512 randomly selected molecules (Tanimoto distance of each compound to these 512 selected molecules) (3) Apply K-means clustering using Euclidean distance (4) Randomly assign 20% of clusters to the OOD test set and the remainder to the ID data set Max Dissimilarity (1) Perform K-means clustering