This section again illustrates how the quantitative benchmark for regression in the form of atomic contributions from the Crippen logP model can be used for probing specific behavior of an XAI method + ML model combination. In particular, we saw how the benchmark set was used to identify problematic behavior w.r.t. how the XAI method evaluated some of the atomic contributions. This type of error probing is crucial in order to improve XAI methods applied to chemistry. Furthermore, we saw how one can not assume better XAI performance simply from working with a more accurate model; explainability and model performance is not necessarily correlated.