Notably, many molecular properties can also be naturally described by Equation . For The sum pooling GNNs are similar in embedding, regardless of the number of convolutional layers and, notably, regardless of whether they are trained or untrained. In our experiments, it was possible to differentiate between max pooling and sum pooling GNNs with CKA linear . In the max pooling GNNs, the trained and untrained GNNs were not similar in embedding for the less challenging data sets (Veith et al.). This is due to the fact that in max pooling only the maximal (i.e., the most important) part of the node representations is passed on. In contrast, for two out of three challenging data sets from Carbon-Mangel et al., the max pooling models could not differentiate between active and inactive compounds. More specifically, for the CYP2C9 substrate/non-substrate data set, the MCC obtained across all seeds was less than 0.001, and for the CYP3A4 substrate/non-substrate data set, the MCC averaged over the five seeds was 0.075 ± 0.066. We postulate that this low performance leads to similarities between the trained and untrained embeddings in max pooling GNNs for these two data sets. For the average MCC values of all models, the reader is referred to the supporting information. The physicochemical property vectors are more similar to the embeddings from the sum pooling GNNs than those from the max pooling GNNs. This is apparent from Figure and is confirmed by calculating the ranking of the representation similarity. We postulate that this similarity stems from the similar functional form exhibited