We examined "descriptor collision" for several chemical fingerprint systems (MDL 320, Daylight, SMDL), and for a 2D-based descriptor set. For large databases (ChemNavigator and WOMBAT), the smallest collision rate remains around 5%. We systematically increase the "descriptor collision" rate (here termed "descriptor confusion"), in order to design a set of "descriptors to mask chemical structures", DMCS. If effective, a DMCS system would not allow third parties to determine the original chemical structures used to derive the DMCS set (i.e., reverse engineering). Using SMDL keys, the "confusion" rate is increased to 45.6% by eliminating those keys that have a low frequency of occurrence in WOMBAT structures. We applied an automated PLS engine, WB-PLS [Olah et al., J. Comput. Aided Mol. Des., 18 (2004) 437], to 1277 series of structures from 948 targets in WOMBAT, in order to validate the biological relevance of the SMDL descriptors as a potential DMCS set. The "reduced set" of SMDL descriptors has a small loss of modeling power (around 20%) compared to the initial descriptor set, while the collision rate is significantly increased. These results indicate that the development of an effective DMCS is possible. If well documented, DMCS systems would encourage private sector data release (e.g., related to water solubility) and directly benefit public sector science.