In this research, we hypothesize that test-time augmentation has a difference between in-domain and out-of-domain predictions. It remains to be seen if these differences can be used to determine out-of-domain predictions, especially in the field of NLP-based molecular representations. This is because we find less variation in out-of-domain in-between sample interpretations than the variation of in-domain interpretations. This indicates that if the epistemic uncertainty is measures the same model effects as XAI methods described here, it will show decreased values in uncertainty for out-of-domain samples. Additionally, this result identifies the need to combine XAI explanations with applicability domain assessments, to verify the explanation.