Adaptive search methods are often used to design urban water distribution networks when the number of pipes in the network is insignificant. For complex, real-world networks, however, such methods are computationally demanding and they have difficulty finding near-global optima. To identify a solution as close to the global optimum (and in which no pipe can be reduced without violating pressure constraints), requires a high-speed computer potentially running for a long time and also probably some good fortune. This work presents a methodology for refining the solutions found by adaptive search algorithms used in the design of large waterdistribution networks. The approach employs two heuristics to search for an optimal combination of pipes that, after a reduction of their diameters, will maximize cost savings while continuing to meet design constraints. The post-optimization approach presented here is shown to be an efficient complement to heuristic search algorithms used in the design of real-world networks.