Today customers want to use powerful search engines for their huge and increasing content repositories. Full-text-only products with simple result lists are not enough to satisfy this community. Different content sources require different analyzing and indexing strategies and a content-specific result set presentation. There is a lot of research in the field of using semantic web technologies for information retrieval. A wide range of useful standard vocabularies and powerful frameworks have been developed that can be used to gather, transform and store metadata. However, in practise we see a gap between the state of art of information retrieval and customer needs with a defined prise-performance relation. It is a challenge to index a large file server with heterogeneous content annotated with metadata from different vocabularies, to provide an ontology-based navigation, to produce semantic annotated search results, to use faceted browsers as powerful filtering mechanism and do that with an out-of-the-box solution, which is stable, has a good performance and provides a simple way to configure it. With this viewpoint we present in this paper the usage of RDF-based semantic descriptions in an enterprise search solution developed at interface:projects.This paper covers lessons learned from developing a metadata-focused information retrieval system called
. Especially we discuss the challenges and possible solutions in an enterprise (-wide) search scenario, and show the place where semantic descriptions matter in such a solution.