This talk addresses two important issues motivated by of our recent research in SRL. First, is the value of data-driven, "bottom-up" search in learning the structure of SRL models. Bottom-up induction has a long history in traditional ILP; however, its use in SRL has been somewhat limited. We review recent results on several structure-learning methods for Markov Logic Networks (MLNs) that highlight the value of bottom-up search. Second, is the value of transfer learning in reducing the data and computational demands of SRL. By inducing a predicate mapping between seemingly disparate domains, effective SRL models can be efficiently learned from very small amounts of in-domain training data. For example, by transferring a model learned from data about a CS department, we have induced reasonably accurate models for IMDB movie data given training data about only a single person.