We hypothesize two possible reasons for this phenomenon. First, it is possible that the GCN learned to predict clogP not based on the underlying atom contributions, but arrived at the prediction through different reasoning, which would be a version of the clever Hans effect . Second, the generated explanations could be wrong. This can occur, because the subgraphs used in the calculation of the Myerson values can be unknown to the trained GNN and can also be invalid molecules. If the GNN generates nonsensical outputs for these subgraphs, this would perturb the Myerson values. While these subgraphs can be outside the trainings domain of the GNN, so far no game theoretic solution concept is able to address the molecular validity of subsets. Figure shows that the Myerson explanations can successfully distinguish between different learning methodologies. Wrong explanations because of invalid molecular fragments therefore play at most a minor role in Myerson values on chemical datasets and the main reason for unexpected explanations is the investigated GNN. This and subsequent analyses show that the Myerson explanations can therefore be seen as correct explanations.