The competition and correlation between network completeness and the associated computational burden when addressing characterization of the chemical space is key to advance the field. Expanding the boundaries of the investigated chemical spaces requires more robust, accurate, and efficient energy regressors, which can be obtained via Machine Learning (ML) acceleration strategies . Ulissi et al. built a surrogate model with GA fingerprints fed to a Gaussian process and combined it with BEP relationships to obtain the rate-limiting steps for the conversion of syngas to C 2 products on Rh catalysts. This model was employed within an active learning strategy while keeping track of uncertainty to predict the most relevant steps to be refined with DFT. An alternative approach involved employing ML potentials from the Open Catalyst Project (OCP) for refining initial guesses of the adsorption geometries subsequently relaxed with DFT , reducing the total computational time. Although speeding-up DFT simulations, these models are still too computationally demanding for high-throughput in-silico exploration. As for the transition state (TS) and corresponding activation energy, going beyond the use of BEPs has been demonstrated via multiple ML regressors on dispersed datasets for C x H y O z species on metals, achieving MAEs in the order of 0.2 eV .