Finding optimal features is important for mediumsized datasets (bias error by featurization reduces when the relation between structure and property can be learned from more data). The customization of atomic and molecular features for a task at hand is an important aspect of model optimization even for deep learning models because the optimal features are not selected automatically. Despite these insights being rather expected, we find that often not enough attention is paid to featurization when building new models.