This　paper　addresses　the　challenge　of　video　search　with　only　a　handful　query　exemplars　by　proposing　a　triplet　ranking　network-based　method.　Based　on　the　typical　scenario　for　video　search　system,　a　user　begins　the　query　process　by　first　utilizing　the　metadata-based　text-to-video　search　module　to　find　an　initial　set　of　videos　of　interest　in　the　video　repository.　As　bridging　the　semantic　gap　between　text　and　video　is　very　challenging,　usually　only　a　handful　relevant　videos　appear　in　the　initial　retrieved　results.　The　user　now　can　use　the　video-to-video　search　module　to　train　a　new　classifier　to　search　more　relevant　videos.　However,　since　we　found　that　statistically　only　fewer　than　5　videos　are　initially　relevant,　training　a　complex　event　classifier　with　a　handful　of　examples　is　extremely　challenging.　Therefore,　it　is　necessary　to　improve　video　retrieval　method　that　works　for　a　handful　of　positive　training　example　videos.　The　proposed　triplet　ranking　network　is　mainly　designed　for　this　situation　and　has　the　following　properties:　(1)　This　ranking　network　can　learn　an　off-line　similarity　matching　projection,　which　is　event　independent,　from　other　previous　video　search　tasks　or　datasets.　Such　that　even　with　only　one　query　video,　we　can　search　its　relative　videos.　Then　this　method　can　transfer　previous　knowledge　to　the　specific　video　retrieval　tasks　as　more　and　more　relative　videos　being　retrieved,　to　further　improve　the　retrieval　performance;　(2)　It　casts　the　video　search　task　as　a　ranking　problem,　and　can　exploit　partial　ordering　information　in　the　dataset;　(3)　Based　on　the　above　two　merits,　this　method　is　suitable　for　the　case　where　only　a　handful　of　positive　examples　exploit.　Experimental　results　show　the　effectiveness　of　our　proposed　method　on　video　retrieval　with　only　a　handful　of　positive　exemplars.