Computational Methodology: To generate the CREMP data set, we utilized the CREST tool to explore diverse conformational ensembles of macrocyclic peptides. We began with a set of 36,198 unique homodetic macrocyclic peptides, sampled based on key parameters such as side chains, stereochemistry, and N-methylation. Using RDKit, each peptide was converted to canonical SMILES, and initial conformers were generated via the ETKDGv3 method, followed by energy optimization using the MMFF94 force field. The 1,000 lowest-energy conformers were further optimized using GFN2-xTB in chloroform solvent. These optimized structures were then used as inputs for CREST simulations, where 14 metadynamics runs were performed in parallel. The final ensembles were filtered to ensure chemical graph consistency across all conformers.