Decentralized Online Social Networks (OSNs) attempt to improve user privacy and security. One example is Vegas, a Peer-to-Peer (P2P) OSN which attempts to bring its users back into complete control of the data they share. Due to its decentralized characteristics, P2P OSNs cannot support social search functions in the same way users of centralized OSNs like Facebook are familiar with. Well-known and efficient P2P search algorithms cannot always be applied as knowledge about the structure of the social graph can be very limited. In this paper, we present an in-depth analysis of forwarding strategies to enable social search for secure and privacy preserving P2P OSNs. We compare well-known metrics from the field of unstructured P2P networks with metrics from the area of social network analysis and evaluate their applicability for P2P OSNs like Vegas. We simulate all metrics on four distinct datasets which were generated artificially from the ER- and the BA-model and from crawling data of Lastfm and Flickr. Our evaluation shows that prioritization based on knowledge from the ego network often yields the best results.