Simulating chemical reactions is essential to developing a fundamental understanding of their mechanism and predicting experimental outcomes. Machine learned potentials (MLPs) offer an enticing approach to such simulations, enabling the efficient mapping between nuclear configurations and energies (R → E). Moreover, in contrast to classical force fields, they offer flexibility and systematic improvability. In the limit of correct forces and converged sampling, such simulations should afford access to the accurate estimation of rate and equilibrium constants. However, despite the development of Gaussian Approximation Potentials (GAPs) and high dimensional neural network potentials (NNPs) more than ten years ago, MLPs are still yet to find routine use for chemical reaction simulation. This slow uptake is likely due to the computational and time investment required to train reactive potentials for new systems.