SGGS (Semantically-Guided Goal-Sensitive reasoning) is a refutationally complete theorem-proving method that offers first-order conflict-driven reasoning and is model complete in the limit. This paper investigates the behavior of SGGS on Horn clauses, which are widely used in declarative programming, knowledge representation, and verification. We show that SGGS generates the least Herbrand model of a set of definite clauses, and that SGGS terminates on Horn clauses if and only if hyperresolution does. We report on experiments applying the SGGS prototype prover Koala to Horn problems, with promising performances especially on satisfiable inputs.