Many studies have indicated that the predictive validity of partworth estimates in conjoint analysis can be improved by imposing constraints on utility estimates. In this paper, two types of constraints are investigated: within- and across-attribute. Prior information can be imposed (i.e. a priori order for quantitative variables such as Price) or collected during the interview (i.e. self-explicated rankings and importance ratings). The authors (van der Lans, Wittink, Huber and Vriens) investigate the effect of imposing constraints on conjoint utilities for full-profile and ACA. Hit rates for hold-out concepts are shown to increase significantly for full-profile when utility constraints are imposed. ACA hit rates on average are higher than full-profile, and are not significantly improved by imposing constraints. Originally published in 1992 Sawtooth Software Conference Proceedings.