The fact that fingerprints encode the structural aspects and similar molecules can exhibit the same biological activity sets the groundwork and the reason to design the cluster-based cross-validation scheme. We use the K-means algorithm to identify homogeneous subgroups or clusters within the data based on the similarity between their fingerprints. Based on this, we use random sampling to create validation sets for k-fold cross-validation.