Dynamic composition of services provides the ability to build complex distributed applications at run time by combining existing services, thus coping with a large variety of complex requirements that cannot be met by individual services alone. However, with the increasing amount of available services that differ in granularity (amount of functionality provided) and qualities, selecting the best combination of services becomes very complex. In response, this paper addresses the challenges of service selection, and makes a twofold contribution. First, a rich representation of compositional planning knowledge is provided, allowing the expression of multiple decompositions of tasks at arbitrary levels of granularity. Second, two distinct search space reduction techniques are introduced, the application of which, prior to performing service selection, results in significant improvement in selection performance in terms of execution time, which is demonstrated via experimental results.