The prevalence of COVID-19 has placed undue burden on the healthcare system, signaling a critical need to develop innovative machine learning (ML) strategies to improve triage and care for patients who are hospitalized with COVID-19. Recent developments in artificial intelligence have shown that deep learning algorithms such as convolutional neural networks (CNN) are effective in automating various tasks in medical imaging such as pneumonia detection [1]. The challenge remains in implementing these algorithms efficiently in the clinic where there may be insufficient hardware to support CNN with hundreds of hidden layers and millions of parameters [2]. To address this challenge, we developed a shallow 3-layer CNN classifier, SimplePNUnet, to detect the presence of pneumonia using a subset of 2000 frontal chest X-rays from the publicly accessible dataset released as part of the 2018 RSNA Pneumonia Detection Challenge. We fine-tuned model hyperparameters using Bayesian optimization. Additionally, we assessed the robustness of SimplePNUnet across different downsampling techniques and batch sizes. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUROC). The performance of the best model across the training (n=1000), validation (n=500), and test (n=500) sets was comparable (AUC, 0.870 vs 0.812 vs 0.834, respectively) and within range of U-Net based pneumonia detection algorithms (AUC, 0.80-0.90). SimplePNUnet achieved good performance on an external validation set (AUC, 0.712).