In TREC 2006, we participate in three tasks of the Terabyte and Enterprise tracks. We continue experiments using Terrier1, our modular and scalable Information Retrieval (IR) platform. Furthering our research into the Divergence From Randomness (DFR) framework of weighting models, we introduce two new effective and low-cost models, which combine evidence from document structure and capture term dependence and proximity, respectively. Additionally, in the Terabyte track, we improve on our query expansion mechanism on fields, presented in TREC 2005, with a new and more refined technique, which combines evidence in a linear, rather than uniform, way. We also introduce a novel, low-cost syntacticallybased noise reduction technique, which we flexibly apply to both the queries and the index. Furthermore, in the Named Page Finding task, we present a new technique for combining query-independent evidence, in the form of prior probabilities. In the Enterprise track, we test our new voting model for expert search. Our experiments focus on the need for candidate length normalisation, and on how retrieval performance can be enhanced by applying retrieval techniques to the underlying ranking of documents.