A new analysis method, based on the use of DPA clustering, allows for creating high-dimensional conformational free energy surfaces in a gridless, computationally accessible way, allowing the conformational ensembles of highly flexible molecules to be characterized in a systematic and efficient way which scales better than the conventional grid-based approach. Pairing DPA's density estimation tool with a dataset of configurations generated through concurrent well-tempered metadynamics simulations allows for quantitative per-point FES generation through a simple Zwanzig-based reweighing scheme. DPA's classification approach further allows for automatic, high-dimensional interpretation of these FESes. This approach has been demonstrated for systems with 2, 4, and 11-dimensional conformation spaces, and the performance of this method was tracked using a set of consistency metrics that enable its application in realistic cases. The approach is entirely simulation agnostic, operating solely on the coordinates of configurations in conformation space and their corresponding biases. We thus envision applications for simulations performed at a broad range of theory levels and across various physical environments. The code developed here is fully open source and available from .