We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable convolutional neural network models using biological sequences. BioNAS introduces knowledge dissimilarity functions to enable the joint optimization of predictive power and biological knowledge when searching architectures. By optimizing the consistency with existing knowledge, we demonstrated that BioNAS optimal models revealed novel knowledge in both simulated data and in real data of functional genomics. In a simulated multitask learning framework, BioNAS robustly uncovered the masked knowledge by generating child networks with high knowledge consistency of existing unmasked knowledge. Furthermore, we applied BioNAS to the task of predicting the protein-RNA binding sites using the real data of ENCODE eCLIP. BioNAS augmented eCLIP model training with information from RNAcompete, and facilitated the annotation of convolutional feature maps with biological semantics. In sum, BioNAS provides a useful tool for domain experts to incorporate their prior belief into an automated machine learning framework. BioNAS is available at https://github.com/zj-zhang/BioNAS-pub.