Predictive models enable prioritization of compounds with otherwise unknown properties and facilitate cost-effective discovery of promising candidate compounds. Further, data scientists can use explainable artificial intelligence (XAI) methods to gain insights into the reasoning underlying the models and identify chemical regions of interest. XAI techniques aim to unveil information hidden in ML models that Figure Data scientists create models that predict molecular properties and XAI reveals logic connecting substructures to the prediction: (left) a compound of interest is selected for inspection; (center) contributions for the predicted property of interest are calculated with an XAI method that delivers one score for each atom; (right) overlaying a molecular structure with those atom-scores.