Multiobjective heuristic search in state space graphs generally aims at determining the exact set of Pareto-optimal cost vectors associated to solution paths or an approximation of this Pareto set. When a preference model is available under the form of scalarizing function f, the search effort merely focuses on the direct determination of an f-optimal tradeoff within the Pareto-set. The solution algorithms proposed in this area rely on uni-directional approaches that develop a front of Pareto-optimal labels attached to subpaths from the initial node s to the goal nodes. When there is a single goal node t, explicitly known, and when search operators are reversible, a bidirectional search approach is possible, interleaving and interacting searches, forward from s and backward from t. Although the respective advantages of unidirectional approaches versus bidirectionnal approaches have been widely discussed in the single objective case, they have not been investigated inthe setting of multiobjective search. The aim of this paper is to propose a first implementation of multiobjective bidirectional heuristic search and to empirically assess his value compared to unidirectional search. Our comparison concerns both the determination ofthe entire Pareto set and the determination of OWA-optimal tradeoffs within the Pareto set. We provide results of numerical tests performed on random instances of path-planning problems, both in terms of number of nodes expanded and in terms of computation times.