Allergic contact dermatitis (ACD) is a widespread cause of workers' disabilities. Although some substances found in the workplace are rigorously tested, the potential of the vast majority of chemicals to cause skin sensitization remains unknown. At the same time, exhaustive testing of all chemicals in workplaces is costly and raises ethical concerns. New approaches to developing information for risk assessment based on computational (quantitative) structure-activity relationship [(Q)SAR] methods may be complementary to and reduce the need for animal testing. Virtually any number of existing, de novo, and even preconceived compounds can be screened in silico at a fraction of the cost of animal testing. This work investigates the utility of ACD (Q)SAR modeling from the occupational health perspective using two leading software products, DEREK for Windows and TOPKAT, and an original method based on logistic regression methodology. It is found that the correct classification of (Q)SAR predictions for guinea pig data achieves values of 73.3, 82.9, and 87.6% for TOPKAT, DEREK for Windows, and the logistic regression model, respectively. The correct classification using LLNA data equals 73.0 and 83.2% for DEREK for Windows and the logistic regression model, respectively.