Knowledge-based descriptors extracted from composite crystal-field environments in crystal data have been developed for the description of interaction properties of small molecules. Using SuperStar seven diverse probe atoms have been selected to reflect the most important physicochemical properties. The general application of these descriptors in comparative molecular field analysis has been investigated using a dataset of thermolysin inhibitors, and a comparison to the GRID program has been performed. We especially focused on hydrogen-bond donor and acceptor properties by selecting a carbonyl and amino group as suitable probes. Their performance has been compared to that of the hydrogen-bond descriptors presently implemented in CoMSIA (comparative molecular similarity indices analysis). The newly developed descriptors produced significantly improved statistics for the correlation analyses if they are exclusively applied or, even better, applied in combination with other CoMSIA descriptors. Two methodologically different approaches have been tested to approximate the developed descriptors. Both reduce significantly the required computational efforts in particular for large data sets. The graphical interpretation of the field contributions of hydrogen-bonding properties elucidates additional features compared to those obtained from the original CoMSIA method. They are of valuable support for the design of improved inhibitors.