To conclude the overview, it is clear that template-based models exhibit the best performance on the USPTO-50k dataset. They have the highest round-trip metrics while proposing a diverse set of predictions with practically no invalid molecules and a low number of duplicates. For databases of small to medium size, this type of model is preferred. It is to be noted that the benchmarking does not provide a measure of reaction "novelty" for frameworks. Hence, it cannot be concluded that template-based models propose a higher degree of "unexplored" or "unreported" reactions. Furthermore, the benchmarking did not provide a conclusive explanation for the inferior performance of template-free and semi-template models. A possible reason could be the size of the USPTO-50k database, which could render optimisation of the large parameter space challenging. In other words, do the frameworks overfit the database? Another possibility could be that the deep learning techniques are simply unable to "learn" reaction chemistry -a problem which is solved through reaction templates. This question will be explored in Section 3.3.