Transfer learning with deep neural networks has revolutionized the fields of computer vision and natural language processing in the last decade. This is especially significant for fields such as biology where we usually have small labeled data but an abundance of unlabeled data. Using abundant unlabeled data to enhance performance on a small labeled dataset is the hallmark of transfer learning. In this dissertation, I tap into the potential of transfer learning to solve critical problems in the antibiotic resistance domain. Antibiotic resistance occurs when bacteria gain functionality to thwart mechanisms through which antibiotics work to kill or inhibit bacteria. This resistance is leading to alarming rates of mortality and morbidity among the world population. Two critical aspects in combating antibiotic resistance is searching for novel sources of antibiotics, and identifying genes that confer antibiotic resistance ability to a bacteria. As I show, in both of these cases, we have small labeled datasets but large unlabeled data at our disposal. I have incorporated transfer learning techniques in both cases, significantly improving on current state-of-the-art performance typically achieved by alignment based approaches such as BLAST or HMMER. I also introduce a novel optimization method to train neural networks that offer reliable uncertainty estimates when the model is tested on Out-of-distribution (OoD) data. Finally, I offer future directions on how transfer learning can be further utilized to solve these critical problems.