We utilize an unsupervised learning procedure similar to the one found in the MOLAN workflow by Sivaraman and Jackson et al., which resembles the ButinaSplitter from DeepChem. The procedure is as follows: SMILES representations of each molecule are converted into extended connectivity (Morgan) fingerprints 30 with a radius of 2 and 1,024 bits (ECFP4). The ECFP4 fingerprints are then used to construct a Tanimoto similarity matrix, which enables a clustering of the molecules using the Butina clustering algorithm 31 with a radial cutoff of 0.6 as implemented in RDKit. Clusters with at least 7 molecules are included in the training/test set and otherwise in the out-of-sample set to explore how well the trained machine learning models generalize. For some molecules either the descriptor or RegioSQM20 calculations fail, or the molecules are excluded due to the Chauvenet's criterion, which left us with 21,201 reactions corresponding to 100,588 unique reaction sites. Thus, applying the above procedure results in a training/test set and an out-of-sample set of 15,246 and 5,955 molecules, which corresponds to 73,123 and 27,465 unique reaction sites, respectively. Uniform stratified and random splits are then used to obtain a 80:20 ratio between the training and test sets resulting in 12,196 and 3,050 molecules corresponding to 58,384 and 14,739 unique reaction sites in each set, respectively. For the uniform stratified split, each of the individual clusters are randomly split and hereafter combined to ensure that both the training and test sets have similar representations of the underlying data distribution. On the other hand, the random split is indeed completely random with respect to all of the molecules obeying the cluster size cutoff.