SynTemp can efficiently process data containing 50.000 reactions within a few minutes on personal hardware. Nevertheless, this is insufficient to handle comprehensive reaction databases such as Reaxys ® . For the purpose of classifying reactions, machine learningbased classifiers have been developed that very quickly categorize reactions into predefined classes. These techniques are limited to known reaction types, however, and may overlook novel or rare templates. SynTemp, in contrast, offers the flexibility to categorize reactions without predefined classes, albeit at a much higher computational cost. To overcome these limitations, a combination of hybrid approaches that integrate traditional computational methods with machine learning and the development of incremental learning models that adapt to new reaction data could provide more comprehensive and scalable solutions.