To analyze molecular embeddings created by GNNs, we trained GNNs with varying numbers of convolutional layers (one to four) and compared the representations directly after the readout operation of the message passing base. To show that the similarity between embeddings created by sum pooling is not dependent on the training procedure but rather on the architecture itself, untrained GNNs with an equal number of convolutional layers were also considered. The message-passing base consisted of GINs with two internal layers, each with ReLU activation functions, and 64 node features were used. The MLP classifier also used 64 hidden dimensions, two layers, no batch normalization, and ReLU as the activation function. The networks were trained with the optimizer Adam, a learning rate of 10 -4 , a weight decay of 5 • 10 -4 , and a batch size of 128 for 100 epochs. Each atom was represented by a one-hot encoding of length eleven, describing the atom type (the ten most frequent atom types, i.e., carbon, oxygen, nitrogen, chlorine, sulfur, fluorine, bromine, phosphorus, iodine, and arsenic, plus "other"), concatenated with the number of heavy atoms and the number of neighboring hydrogen atoms.