Contrasting multiple input parameters that represent the same underlying ground truth is a method to assess the stability of a model or method. This test-time data augmentation has been used in prior research conducted in domains including medical imaging, where data augmentation techniques, such as image rotation and contrast adjustment, have been used to measure model uncertainty . These strategies have demonstrated their efficacy in enhancing model performance, gauging model uncertainty, and increasing robustness. However, this methodology remains largely unexplored within the field of XAI concerning molecular representations, presenting an opportunity to assess the robustness of XAI methods.