Furthering the state-of-the-art in adhoc web search is one of the underlying goals for the NTCIR We Want Web (WWW) task. Adhoc search can be viewed as a bridge connecting many of the specialized sub-fields that are a result of the way people connect to and use information access systems. Since this is the first year of the WWW task, and no training data was provided for the English subtask, we focused on classic techniques for improving effectiveness in lieu of modern techniques based on supervised learning. In particular, we explored the use of Markov Random Field Models (MRFs), static document features, field-based weighting, and query expansion. This round we made extensive use of the Indri search system and the flexible query language it provides to produce effective results.