Knowledge compilation algorithms transform a probabilistic logic program into a circuit representation that permits efficient probability computation. Knowledge compilation underlies algorithms for exact probabilistic inference and parameter learning in several languages, including ProbLog, PRISM, and LPADs. Developing such algorithms involves a choice, of which circuit language to target, and which compilation algorithm to use. Historically, Binary Decision Diagrams (BDDs) have been a popular target language, whereas recently, deterministic-Decomposable Negation Normal Form (d-DNNF) circuits were shown to outperform BDDs on these tasks. We investigate the use of a new language, called Sentential Decision Diagrams (SDDs), for inference in probabilistic logic programs. SDDs combine desirable properties of BDDs and d-DNNFs. Like BDDs, they support bottom-up compilation and circuit minimization, yet they are a more general and flexible representation. Our preliminary experiments show that compilation to SDD yields smaller circuits and more scalable inference, outperforming the state of the art in ProbLog inference.