Ride sharing becomes more and more popular not least because internet services help matching offers and request. However, current systems use a rather simple-minded functionality allowing to search for the origin and destination city, sometimes enriched with radial search around the cities. We show that theses services can be substantially improved using innovative route planning algorithms. More concretely, we generalize previous static algorithms for many-to-many routing to a dynamic setting and develop an additional pruning strategy. With these measures it becomes possible to match each request to $n$ offers using $2n+1$ exact travel time computations in a large road network in a fraction of a microsecond per offer. For requests spread over Germany according to population density, we are able to reduce the number of failing entries substantially. We are able to find a reasonable match for more than 60% of the failing entries left by contemporary matching strategies. Additionally, we halve the average waste of resources in the matches found compared to radial search.