Recently, we published an article in this journal that explored physics-based representations in combination with kernel models for predicting reaction properties (i.e. TS barrier heights). In an anonymous comment on our contribution, the authors argue, amongst other points, that deep learning models relying on atom-mapped reaction SMILES are more appropriate for the same task. This raises the question: are deep learning models sounding the death knell for kernel based models? By studying several datasets that vary in the type of chemical (i.e. high-quality atom-mapping) and structural information (i.e. Cartesian coordinates of reactants and products) contained within, we illustrate that physics-based representations combined with kernel models are competitive with deep learning models. Indeed, in some cases, such as when reaction barriers are sensitive to the geometry, physics-based models represent the only viable candidate. Furthermore, we illustrate that the good performance of deep learning models relies on high-quality atom-mapping, which comes with significant human time-cost and, in some cases, is impossible. As such, both physics-based and graph models offer their own relative benefits to predict reaction barriers of differing datasets.