We present a study of the contributions of three classes of ranking signals: BM25F, a retrieval function that is based on words in the content of web pages and the anchors that link to them; SALSA, a link-based feature that takes all or part of the result set to a query as input; and matching-anchor count (MAC), a feature that measures precise matches between queries and anchors pointing to result pages. All three features incorporate both link and textual features, but in varying degrees. BM25F is the state-of-the art exponent of Salton’s term-vector model, and is based on a solid theoretical foundation; the two other features are somewhat more ad-hoc. We studied the impact of two factors that go into the formation of SALSA’s “base” set: whether to use conjunctive or disjunctive query semantics, and how many results to include into the base set. We found that the choice of query semantics has little impact on the effectiveness of SALSA (with conjunctive semantics having a slight edge); more surprisingly, we found that limiting the size of the base set to a few hundred results of high expected quality maximizes performance. Furthermore, we experimented with various linear combinations of BM25F, MAC and SALSA. In doing so, we made a remarkable observation: adding BM25F to a two-way weighted linear combination of MAC and SALSA does not increase performance in any statistically significant way.