Branching variable selection can greatly a ffect the eff ectiveness and efficiency of a branch-and- bound algorithm. Traditional approaches to branching variable selection rely on estimating the eff ect of the candidate variables on the objective function. We propose an approach which is empowered by exploiting the information contained in a family of fathomed subproblems, collected beforehand from an incomplete branch-and-bound tree. In particular, we use this information to defi ne new branching rules that reduce the risk of incurring inappropriate branchings. We provide computational results that demonstrate the effectiveness of the new branching rules on various benchmark instances.