The explosion of published information on the Web leads to the emergence of a Web syndication paradigm, which transforms the passive reader into an active information collector. Information consumers subscribe to RSS/Atom feeds and are notified whenever a piece of news (item) is published. The success of this Web syndication now offered on Web sites, blogs, and social media, however raises scalability issues. There is a vital need for efficient real-time filtering methods across feeds, to allow users to follow effectively personally interesting information. We investigate in this paper three indexing techniques for users' subscriptions based on inverted lists or on an ordered trie. We present analytical models for memory requirements and matching time and we conduct a thorough experimental evaluation to exhibit the impact of critical workload parameters on these structures.