Abstract This paper deals with Decision-Making in the context of multiattribute utility theory and, more precisely, with the problem of effificiently determining the best alternative w.r.t. an agent’s preferences (choice problem). We assume that alternatives are elements of a product set of attributes and that the agent’s preferences are represented by a generalized additive decomposable (GAI) utility on this set. Such a function allows an effificient representation of interactions between attributes while preserving some decomposability of the model. GAI utilities can be compiled into graphical structures called GAI networks that can be exploited to solve choice problems using collect/distribute schemes essentially similar to those used in Bayesian networks. In this paper, rather than directly using this scheme on the GAI network for determining the most preferred alternative, we propose to work with another GAI function, acting as an upper-bound on utility values and enhancing the model’s decomposability. This method still provides the exact optimal solution but speeds up signifificantly the search. It proves to be particularly useful when dealing with choice and ranking under constraints and within collective Decision-Making, where GAI nets tend to have a large size. We present an effificient algorithm for determining this new GAI function and provide experimental results highlighting the practical effificiency of our procedure