Electrochemistry has the power to unlock extremely useful reactions that are otherwise inaccessible. To design next-generation electrochemical technologies -ranging from batteries and fuel cells to electrosynthesis of value-added products -it is imperative to understand and eventually control reactions on the molecular level. Traditionally, studies of (electro)chemical reactivity have been conducted by hand using either trial-and-error experiments or low-throughput molecular simulations. Recent years have seen the development of a range of computational methods to automatically explore chemical reaction networks (CRNs), which are defined by a set of reactions R between species S. CRNs nisms or pruning by the "chemical distance" between species (the number of bonds that must change for a reaction to occur, or the number of reactions required to transform reactants to products) to focus only on starting species and known products of interest, it is possible to create networks capable of elucidating reaction pathways. However, chemical intuition is limited and unreliable when describing new reactive spaces. In electrochemistry, studies of reaction mechanisms and characterization of reaction products are very challenging. Additionally, the linear scaling relations (i.e. Bell-Evans-Polanyi ) that are widely used to predict the rates of families of similar reactions in thermochemistry have not been well established in electrochemistry. As a result, CRN methods that rely on templates or the chemical distance to known products cannot yet be used to study electrochemical reactivity.