In addition to the specific error types addressed, we highlight the importance of avoiding data leakage, which unfortunately is rather common in chemical datasets. Leaked data and the associated overly optimistic reported model performance hamper the development of new models severely, reduce the confidence in machine learning models and delay their application to real world scenarios. We therefore urge the reader to pay increased attention to data splitting when developing models on new datasets.