In a biomimetic spirit, Nature has been a great source of inspiration towards the design of efficient man-made catalysts. Thanks to the power of Darwinian selection, diverse nitrogenases has evolved to enable the conversion of dinitrogen into ammonia under physiological conditions. Nitrogenases have been identified in 13 bacterial phyla and one archaeal phylum. Despite their apparent diversity, most nitrogen fixing enzymes share a structurally similar catalytic M-cofactor consisting of a distinctive MFe7S9-cluster harboring a central six-coordinate carbide and a homocitrate bound to the "unique" M metal (M = Mo, V, and Fe, typically abbreviated as FeMoco, FeVco and FeFeco, respectively), Figure . Despite the complexity of the dinitrogen reduction to ammonia-which requires the sequential delivery of eight protons, eight electrons and ≥ 16 ATP to convert N2 into 2 NH3-nitrogenases catalyze this process at room temperature with turnover frequencies ≤ 5 s -1 . Among the nitrogenases, FeMoco nitrogenase is the most widespread and efficient, making it the most widely studied class. Despite the vested interest in elucidating the catalytic cycle of nitrogenases, characterizing the N2 fixation mechanism at M-cluster has proven to be extremely challenging, primarily because of the intrinsically complex molecular structure, combined with the multitude of plausible mechanistic pathways. In 2003, Seefeldt and co-workers provided strong experimental evidence for a preferential facet on the catalytic unit in FeMoco nitrogenases (Fe2-S2B-Fe6-S5A, Figure ) through a series of mutation studies on a representative example enzyme (PDB: 3U7Q). Since then, various computational studies have been conducted, with the aim of identifying key reaction steps and intermediates, for FeMoco nitrogenases. Figure : The FeMoco unit in 3U7Q nitrogenase. Based on experimental evidence, Fe2 and Fe6 have been identified as the most probable absorption sites for N2. These studies have resulted in extremely valuable mechanistic insights, though collectively, they have, at the same time, highlighted intrinsic limitations of modelling such complex mechanisms, even with state-of-the-art techniques: seemingly innocuous differences in the computational methodologies used, often resulted in markedly diverging pathways being discovered.