Title: MiSAL: Active Learning for Every Budget. Abstract: In supervised Active Learning (AL), the learner can manipulate the labeled training set by choosing examples to be labeled by an oracle. The size of the labeled set is termed budget. Recent years have seen significant progress in this domain in the context of deep active learning. In particular, it has been shown that in general, different families of AL strategies are suitable for high and low budgets. Here we address for the first time the problem of deciding which family of strategies is most suitable for a given budget in a given task. We start from the theoretical analysis of a mixture model, which motivates a computational approach to decide on the most suitable family of methods for the task and budget at hand. We then propose a practical decision algorithm, which determines what family of strategies should be preferred. Using this algorithm, we introduce MiSAL - a mixed strategy active learning algorithm. MiSAL combines AL strategies from different families, resulting in a method that fits all budgets. We support the analysis by an empirical study, showing the superiority of our method when dealing with image datasets.