The coronavirus pandemic broke out at the end of 2019 in Wuhan, China, and soon became a serious problem worldwide. Although vaccines have been developed, it is still a serious problem and a significant number of people are getting infected daily. PCR test and rapid antigen test are the most used COVID-19 detection methods. Rapid antigen test is less sensitive and performs best in symptomatic people. PCR test is significantly more sensitive, but often it is not the first choice due to higher prices and longer waiting time. X-ray is one of the most commonly used illness detection methods, so naturally, the question of whether it can be used for COVID-19 detection arises. It has been shown that chest X-ray images of COVID-19 patients show specific, but hardly noticeable abnormalities. Automatic detection could be of great help to radiologists in diagnosing COVID-19 cases. Convolutional neural networks have shown great potential in solving computer vision problems, including the classification of medical images. In this study, transfer learning is proposed as a method for solving automatic COVID-19 detection problem. For this purpose, four popular models (ResNet50, ResNet101, AlexNet, DenseNet) have been used as feature extractors. They have been trained using the COVID-XRay-5k dataset and an evaluation of performance for each model has been made. The results are encouraging, as they show the promise of using CNN-s in COVID-19 detection. However, due to the limited number of positive examples, it cannot be claimed that the obtained results are reliable. More detailed testing using a larger and more balanced dataset is needed for a more reliable estimation of the accuracy of these models.