We investigate scalable algorithms for automated composition (WSC) of Semantic Web Services. Our notion of WSC is very general: the composition semantics includes background knowledge and we use the most general notion of matching, partial matches, where several web services can cooperate, each covering only a part of a requirement. Unsurprisingly, automatic composition in this setting is very hard. We identify a special case with simpler semantics, which covers many relevant scenarios. We develop a composition tool for this special case. Our goal is to achieve scalability: we overcome large search spaces by guiding the search using heuristic techniques. The computed solutions are optimal up to a constant factor. We test our approach on a simple, yet powerful real world use-case; the initial results attest the potential of the approach.