Many algorithms exist for conformational exploration, divided into two main classes: (i) systematic and (ii) stochastic. Systematic algorithms explore all possible combinations of dihedral torsions in a molecule, usually in set increments, e.g., every 15°. This approach makes them deterministic and thorough, but unless the conformers thus generated are reoptimized, will only give approximations to the true minima. Also, systematic algorithms can only be used for systems with a small number of rotatable bonds, as the number of possible conformers increases extremely quickly with increasing rotatable bond count. Stochastic algorithms attempt to generate a representative ensemble by selecting conformations in a probabilistic fashion. Two methods to accomplish this goal are knowledge-based algorithms and simulations. In a knowledge-based algorithm, structures are generated by randomly assigning values to dihedral torsions that match known low-energy conformations of similar dihedrals in other systems. There are a great variety of implemented knowledge-based algorithms for conformer ensemble generation, but some examples include ConfGen, OMEGA, Conformator, and iCon. For simulations, Monte Carlo methods, molecular dynamics, or metadynamics 2 are used to sample conformations of a system. Stochastic methods can be applied to larger systems than systematic ones can, but because they rely on inherently random processes, they are not deterministic, and may miss important conformers entirely.