We previously introduced a strategy to generate reactive GAPs in an autonomous manner, requiring only hundreds to a few thousand energy and gradient evaluations. Here, we extend this methodology to explore more complex processes and compare it to recently developed MLP approaches (Fig. ). As a starting point, we considered Diels-Alder (DA) reactions because of the available theoretical and experimental data, and their prominence in chemical and biochemical contexts. Initial efforts to apply our original active learning (AL) strategy using GAP regression to model the reaction between ethene + butadiene, R1, proved promising, producing qualitatively reasonable reactive molecular dynamics (MD). However, the quality of these GAPs were not within 1 kcal mol -1 accuracy of the QM reference method, which is required for accurate rate estimation or dynamic studies (Fig. , MAD = 5.3 kcal mol -1 ). We hypothesised that this was due to the exothermicity of this reaction, which requires the MLP to be accurate in a 60 kcal mol -1 energy window. Indeed, using the same strategy and hyperparameters to model a less exothermic H-abstraction reaction (