Due to the inadequacy of attribute-only representations for many learning problems, there is now a renewed interest in algorithms employing first-order logic or restricted variants thereof as their knowledge representation. In this paper, we give a brief overview of the dimensions along which the complexity of learning in such representations can be controlled. We then present RDT, a model-based learning algorithm for function-free Horn clauses with negation that introduces two new means of complexity control, namely the use of syntactic rule models, and the use of a task-oriented domain topology. We briefly describe some preliminary application results of RDT within the knowledge acquisition system MOBAL, and present directions of further research.