Image retrieval with relevance feedback suffers from the small sample problem. Recently, SVM active learning has been proposed to tackle this problem, showing promising results. However, a small but sufficient number of initially labelled samples are still required to ensure the subsequent active learning efficient and good retrieval performance. In the existing method, the user is asked to label more images before active learning starts. In this paper, a method of embedding Euclidean search into SVM active learning is proposed. With the help of Euclidean search, not only the adverse effect on retrieval performance due to lack of initially labelled samples can be reduced, the retrieval performance can be further enhanced when there is sufficient number of initially labelled samples. Experimental results demonstrate the improvement by the proposed method, especially when the number of initially labelled samples is small.