Bayes networks are directed acyclic graphs where nodes represent events and edges represent probablistic dependencies among events. Associated with each node are conditional probabilities of the associated event triggering if its ancestor nodes trigger. The total probability mass of a lea( node triggering can be computed from simple probability theory, albeit the number of minterms in the formula is exponential in the number of ancestor nodes of that leaf. It is a well-known result that for a large class of networks, a number of minterms only linear in the number of ancestor nodes contributes about 67% of the total probability mass. The problem of Bayes net search is to generate only these high-mass minterms. We introduce a. concurrent algorithm for attempting this, based on converting the net into a concurrent process network. Each parent node sends messages containing partial minterms to child nodes. The novel idea. is to prioritize these messages to give higher weight to partial terms that a.re likely candidates for inclusion in the final high-mass minterms. We have implemented this algorithm in KLl and discuss its attributes.