This work demonstrates how Pynta, an automated kinetics workflow tool, can be combined with reactive machine learning potentials (rMLPs) to accelerate the calculation of reaction barriers and transition state geometries for several surface-mediated reactions, simultaneously. Specifically, using the industrially relevant process of methanol partial oxidation catalyzed by Ag(111) as an example, we showcase an automated concurrent learning protocol to train a system specific rMLP on a diverse set of 53 reactions. The total model obtained a 92% success rate, but investigations into the failed reactions revealed that data bias may have resulted in model degradation. Next, to overcome these limitations, we used a reaction class approach which outperformed the total model approach, and we obtained valid transition states from the entire set of 53 reactions. By comparing these results with a pure Pynta/DFT strategy, we show that our reaction class approach is seven times faster and finds essentially identical activation barriers. Finally, we show that Pynta can be accelerated with a fine-tuned foundational model. Specifically, we demonstrate that fine-tuning (18) Xie, W.; Xu, J.; Chen, J.; Wang, H.; Hu, P. Achieving Theory-Experiment Parity