Figure shows a FES for alanine dipeptide computed from a non-concurrent WTMetaD simulation using what will be referred to as the conventional method . The WTMetaD biases are deposited in the 2D conformational space, defined by two torsions, ϕ and ψ (as illustrated in Figure ), ensuring that the entire space is fully sampled over the course of the simulation. The space is split into a 100 × 100 bins histogram. The distribution of MD configurations throughout the histogram follows the system's natural probability distribution as distorted by the metadynamic bias. The total bias deposited in each bin is known, allowing this distortion of the probability distribution to be reweighted. The resulting FES has a resolution equal to the fineness of the grid, in this case (2π)/100 rad. This methodology is robust but scales poorly to higher dimensional FESes. Both the bias deposition during the simulation and the estimate of the probability distribution require the construction of a grid with the same dimensionality as the conformation space. If the same resolution is desired, increasing the number of torsions incurs an exponential cost on computational resources, rapidly becoming unfeasible. In the scientific literature, this issue is addressed by employing dimensionality reduction methods such as SketchMap . In these methods, the conformational probability density is obtained by histogramming sampled configurations in a lowdimensional space of unphysical coordinates. While this approach can be effective for relatively small systems , its ability to resolve degeneracies and identify conformers when the conformational spaces are defined by a large number of torsional degrees of freedom is not straightforward.