While deep learning methods were initially developed for broad applicability across various types of chemical structure depictions, ChemPix was explicitly designed to recognise hand-drawn chemical structure drawings. One notable constraint of ChemPix is its limited functionality, as it exclusively handles drawings of hydrocarbons and is unsuited for other classes of chemical structure representations. In our recently published study about the DECIMER Image Transformer , we provided evidence to show that even though our deep learning model was not explicitly trained on hand-drawn chemical structure representations, it exhibits a (limited) capability to interpret them. When compared with ChemPix, our model is capable of recognising various hand-drawn representations of small molecule structures that go beyond those of hydrocarbons. Furthermore, our findings suggest that the recognition performance of this model could be enhanced by training it on a dataset that contains a wide range of hand-drawn chemical structure images.