In the following, we briefly recap the methodological bases of two biased enhanced sampling methods representative of the static and adaptive categories: Umbrella Sampling (US) and metadynamics (MetaD). Here we do not report on sampling methods based on constrained dynamics such as the string method used by Santiso et al. to model crystallization both from the melt and from solution , and we refer the interested reader to the original publications for an overview of this method, related to US and MetaD. Biased enahnced sampling methods depend crucially on the choice of low-dimensional descriptors of the system configuration -i.e. the collective variables (CVs) -that in biased sampling are used to define the bias potential . We briefly discuss this point at the end of this section and for a comprehensive overview, we refer the interested reader to reviews on this topic from Giberti et al. and Neha et al. . These types of simulations are typically expensive, especially if multiple CVs are biased. Very many (US) and/or very long (MetaD) simulations may be necessary to obtain convergent thermodynamics and kinetics. However, the computational cost is much lower than if one were to observe crystal nucleation spontaneously in such systems. US can be performed in the most commonly available MD engines that allow for implementation of harmonic restraints; though the CVs typically used to simulate nucleation are unlikely to be available in standard MD codes. A useful and noteworthy plugin is the PLUMED software which interfaces with most MD engines and offers a wide range of CVs, including the ones applicable to study crystal nucleation and discussed in this review. PLUMED also allows the practitioner to perform US and MetaD as well, as other types of biased enhanced sampling, in their favourite MD engine that has been patched with PLUMED.