Active Learning has been a popular method to circumvent the labeling cost in machine learning methods. The majority of active learning approaches can be classified into two categories: representative-based and informative-based methods, with some hybrid methods that combine both. This work presents a naïve query strategy, namely Similarity-Based Active Learning (SBAL), which computes the sum of a row in the similarity matrix at each selection step, and a general optimization framework that can accommodate a broad range of active learning algorithms. The label complexity for different classification metrics is used as a primary criterion for comparing different algorithms. The proposed algorithm’s numerical performance is illustrated using simulated data scenarios and by applying it to the real-world COVID-19 image classification. The results demonstrate that, based on the classification metric, labeling cost and label complexity, SBAL outperforms other hybrid methods, such as Adaptive Active Learning (AAL) and Maximizing Variance for Active Learning (MVAL). •A general optimization framework for a wide range of query strategies.•Extended label complexity to measure the performance of active learning.•The naïve Similarity-Based Active Learning query strategy.