Auto-Focusing is one of the key issues in automatic microscopy. The traditional gradient based auto-focusing algorithms may fail to find the optimal focal plane under the circumstances with low image content density because the slope variation of the focus measure of low content density images is small, and the global maximum may be drowned in noises. This paper proposes a content importance factor based focus measure for guiding automatic search of the optimal focal plane with low image content density. The proposed method classifies the pixels into three types: the content pixels, the debris pixels, and the background pixels, according to the relative variation of gradient magnitude of current image and the reference image captured at different z-axis positions from the same scene and adaptively assigns different weights to pixels based on the image content in the focus measure computation. In this way, the contribution of the content pixels is emphasized while that of debris pixels and background pixels is suppressed, and thus, the steepness of the focus curve around the optimal point is improved. The experimental results show that performance of the proposed method is far superior to the traditional methods: the auto-focusing success rate of the proposed method is larger than 90% under the circumstances with low image content density while the traditional method only gains a success rate of 24%.