We have expanded the ligand knowledge base for monodentate P-donor ligands (LKB-P, Chem. Eur. J. 2006, 12, 291−302) by 287 ligands and added descriptors derived from computational results on a gold complex AuClL. This expansion to 348 ligands captures known ligand space for this class of monodentate two-electron donor ligands well, and we have used principal component analysis (PCA) of the descriptors to derive an improved map of ligand space. Potential applications of this map, including the visualization of ligand similarities/differences and trends in experimental data, as well as the design of ligand test sets for high-throughput screening and the identification of ligands for reaction optimization, are discussed. Descriptors of ligand properties can also be used in regression models for the interpretation and prediction of available response data, and here we explore such models for both experimental and calculated data, highlighting the advantages of large training sets that sample ligand space well.