©　2015,　Springer-Verlag　London.　Semantic　search　or　text-to-video　search　in　video　is　a　novel　and　challenging　problem　in　information　and　multimedia　retrieval.　Existing　solutions　are　mainly　limited　to　text-to-text　matching,　in　which　the　query　words　are　matched　against　the　user-generated　metadata.　This　kind　of　text-to-text　search,　though　simple,　is　of　limited　functionality　as　it　provides　no　understanding　about　the　video　content.　This　paper　presents　a　state-of-the-art　system　for　event　search　without　any　user-generated　metadata　or　example　videos,　known　as　text-to-video　search.　The　system　relies　on　substantial　video　content　understanding　and　allows　for　searching　complex　events　over　a　large　collection　of　videos.　The　proposed　text-to-video　search　can　be　used　to　augment　the　existing　text-to-text　search　for　video.　The　novelty　and　practicality　are　demonstrated　by　the　evaluation　in　NIST　TRECVID　2014,　where　the　proposed　system　achieves　the　best　performance.　We　share　our　observations　and　lessons　in　building　such　a　state-of-the-art　system,　which　may　be　instrumental　in　guiding　the　design　of　the　future　system　for　video　search　and　analysis.