At low temperature, conformers are expected to occupy the thermodynamically most stable configuration on the PES or at least some kinetically-trapped low-lying metastable states. Therefore, from a computational perspective relevant local minima (LM) are usually searched on the rugged, high-dimensional PES and theoretical IR spectra, commonly computed with DFT including exact exchange at the hybrid level for sufficient accuracy, are compared to the experimentally observed spectra. The exploration of the PES is typically performed with molecular dynamics (MD), relying on classical force fields, semiempirical or first-principles potentials in combination with replica-exchange and/or simulated annealing (SA) to enhance sampling. Though highly successful in many cases, this approach based on traditional quan-tum chemical tools suffers from severe drawbacks and limitations: On one hand, the quantitative identification of lowest energy structures at low temperature poses stringent accuracy demands to provide a correct energetic ordering in the 0-2.5 kcal/mol observation range for biomolecules that are characterized by complex interatomic and noncovalent interactions. This imposes the use of higher level computational methods for the determination of realistic relative energetics, while force fields or semiempirical approaches often fail at providing the necessary accuracy. On the other hand, even small peptides contain of the order of tens to hundreds of atoms, making higher level firstprinciples calculations time-consuming for all but the smallest molecules. In particular, using first-principles MD requires long simulation times with ten thousands of energy and force evaluations for typical simulated annealing runs and, in spite of multiple runs with different starting geometries and varying simulated annealing protocols (in terms of highest temperature, simulation length at Tmax and subsequent cooling rate), can potentially fail to recover the most stable structures due to the presence of high-energy barriers on the PES. Even when successful, DFT-MD based identifications of the lowest-energy structures observed in experiments can take several months and might only be practicable for larger systems when introducing experimental information to guide the search. Here, we tackle the task of rapidly finding the global minimum (GM) as well as low-lying LM, with the help of surrogate based genetic algorithms (GA). By leveraging evolutionary mechanisms, GAs have shown efficiency in solving highly nonlinear and complex global optimization problems where deterministic or analytical methods fail at finding correct solutions or efficiently search enormous solution spaces. In particular, the capabilities of GAs were for instance found to surpass SA in the search for ground state fullerene clusters, or perform better at protein structure predictions compared to Monte Carlo approaches on simplified energy models. GAs are also among the most CPU-/search-efficient methods to computationally identify low-energy conformations when applied to small organic molecules or peptides in the gas phase. For example, they outperformed systematic and random search methods for the mycophenolic acid drug-like ligand, and were more efficient than replica-exchange MD for dipeptides in terms of low-energy conformational coverage (respectively within 5 and 10 kcal/mol from the GM). Despite this algorithmic gain, the predictive power of such evolutionary methods evidently depends on whether the energy function is able to faithfully describe the relevant physical interactions. For example, up to now, the lack of fast and sufficiently accurate (free) energy models explains why ab initio protein folding predictions have met little success in recovering secondary and tertiary structures in close agreement with native-like conformations. In that case the enormous space defined by the number of structural degrees of freedom severely challenges search engines, so that protein folding approaches often privilege sequence homology or machine learning 6,60 algorithms. However, in the mid-size range of peptidic systems, GA applications have a lot of potential if tractable energy models with sufficient accuracy exist.