Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) for wide-area search is a difficult problem for both classic techniques and state-of-the-art approaches. Deep Learning (DL) techniques have been shown to be effective at detection and classification, however they require significant amounts of training data. Sliding window detectors with Convolutional Neural Network (CNN) backbones for classification typically suffer from localization error, poor compute efficiency, and need to be tuned to the size of the target. Our approach to the wide-area search problem is an architecture that combines classic ATR techniques with a ResNet-18 backbone. The detector is dual-stage and consists of an optimized Constant False Alarm Rate (CFAR) screener and a Bayesian Neural Network (BNN) detector which provides a significant speed advantage over standard sliding window approaches. It also reduces false alarms while maintaining a high detection rate. This allows the classifier to run on fewer detections improving processing speed. This paper’s focus tests out the BNN and CNN components of HySARNet through experiments to determine their robustness to variations in graze angle, resolution, and additive noise. Synthetic targets are also experimented with for training the CNN. Synthetic data has the potential to allow for the ability to train on hard to find targets where little or no data exists. SAR simulation software and 3D CAD models are used to generate the synthetic targets. This paper focuses on the utilization of the Moving and Stationary Target Acquisition (MSTAR) dataset which is the widely used, standard data set for SAR ATR publications.