Another prominent reason for a deceptively good performance is data leakage, where the test set is too similar to the training set. Rigorously splitting a dataset into training, validation and test sets is a crucial task that can be overlooked easily, and may lead to drastically wrong reported performances. In the following, we showcase this pitfall by training a model of the QM9 target internal energy at temperatures T equal 0 K and 298 K. We treat the temperature as an input (in addition to the molecular graph), and train on the single property U (T ). The temperature is appended to the aggregated molecular embedding (after the message-passing neural network, before the feed-forward neural network). If all datapoints are treated as independent, a massive amount of data leakage occurs since many molecules in the test set also occur in the training set, albeit at a different temperature. The left panel of Fig. , depicts the true (only test set datapoints without leakage) and perceived (datapoints with and without leakage) performance for test sets with a different number of datapoints that are leaked. Depending on the constitution of the test set, different mean absolute errors are observed. For test sets with a large amount of leakage, the model appears to perform deceptively well. This perceived performance does not depict the intended application case, where the model is supposed to predict the internal energy of a new molecule outside the training set. The right panel of Fig. shows the distribution of the absolute errors of test datapoints that are held out during trainig versus datapoints leaked from the training set, where again the distribution of errors for leaked datapoints does not reflect reality, i.e. the distribution of errors for new, independent molecules.