Content syndication has become a popular way for timely delivery of frequently updated information on the Web. Today, web syndication technologies such as RSS or Atom are used in a wide variety of applications spreading from large-scale news broadcasting to medium-scale information sharing in scientific and professional communities. However, they exhibit serious limitations for dealing with information overload in Web 2.0. There is a vital need for efficient real-time filtering methods across feeds, to allow users to effectively follow personally interesting information. We investigate in this paper three indexing techniques for users' subscriptions based on inverted lists or on an ordered trie for exact and partial matching. We present analytical models for memory requirements and matching time and we conduct a thorough experimental evaluation to exhibit the impact of critical parameters of realistic web syndication workloads.