Visual chirality measures the distribution variation of visual data under transformation, while it has not been explored in freehand sketches yet. In this paper, we investigate the vertical flipping associated with visual chirality in freehand sketches. Our analysis of investigation results reveals that the vertical flipping shows a high degree of visual chirality. To utilize the high-level cues automatically discovered by predicting the vertical flipping, we propose a Visual Chirality Attention (VCA) module for deep CNNs, which consists of two sequential sub-modules: channel and chirality attention. Experimental results of sketch recognition on TU-Berlin dataset show that our method performs more favorably against state-of-the-art attention-based methods. Our code can be found at https://github.com/zhengyinghit/VCANet.