We have approached the reaction network completeness with a set of templates including all potential bond-breaking reactions leading to all the available intermediate fragments, even those that might be discarded initially based on chemical intuition. CARE is modular in nature, allowing the user to implement new templates for increasing the granularity in the definition of the explored chemical space. This systematic approach reduces bias in CRNs, but leads to larger reaction lists that can be a bottleneck when analyzing and manipulating the constructed networks. However, there are three definitive advantages in the methodology. The first is the modularity of the algorithm, allowing the implementation of additional molecular and reaction templates, thus getting as close to network completeness as desired, for instance, by including competing reactions on surfaces and solution. CARE can consider rare events easily, providing the possibility for studying their implication for the origin of catalyst deactivation , an aspect typically overlooked by traditional DFT simulations but crucial for improving the long-term stability of the catalyst. The third element is the substantial reduction of the computational cost by accelerating the energy evaluation of the reaction networks with GAME-Net-UQ.